CN111164706B - Disease-associated microbiome characterization processes - Google Patents

Abstract

Embodiments of methods and/or systems for characterizing one or more microorganism-related conditions may include: determining a microorganism dataset associated with a set of subjects; and applying an analysis technique to perform a characterization process for one or more microorganism-related conditions based on the microorganism dataset, using the set of microbiome characterization modules.

Description

Disease-associated microbiome characterization process

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Serial No. 62/582,191, filed on November 6, 2017, U.S. Provisional Application Serial No. 62/545,039, filed on August 14, 2017, and U.S. Provisional Application Serial No. 62/658,308, filed on April 16, 2018, the entire contents of each of which are incorporated herein by reference.

Technical Field

The present disclosure relates generally to genomics and microbiology.

Background Art

The microbiome can include an ecological community of commensal, symbiotic and pathogenic microorganisms associated with an organism. The characterization of the human microbiome is a complex process. The human microbiome includes more than 10 times more microbial cells than human cells, but the characterization of the human microbiome is still in its early stages, for example, due to limited sample processing techniques, genetic analysis techniques and resources for processing large amounts of data. Current knowledge has clearly established the role of microbiome associations with a variety of health conditions, and has become a medium for the increasing attention of host genetics and environmental factors to the development of human diseases. It is suspected that the microbiome plays at least a partial role in many health/disease related states (e.g., preparation for childbirth, diabetes, autoimmune disorders, gastrointestinal disorders, rheumatoid disorders, neurological disorders, etc.). In addition, the microbiome can mediate the impact of environmental factors on human, plant and/or animal health. Considering the profound revelation of the microbiome in affecting the health of the subject, work related to the characterization of the microbiome, the generation of insights from the characterization and the generation of treatments configured to correct the dysbiosis state should be pursued. However, the current methods and systems for analyzing the human microbiome and/or providing therapeutic measures based on the insights obtained leave many unanswered questions.

Thus, there is a need in the field of microbiology for new and useful methods and/or systems for characterizing, monitoring, diagnosing and/or intervening in one or more microbial-related health conditions and/or associated relationships (e.g., specific characteristics associated with microorganisms and/or conditions, etc.), such as for individualized use and/or for use in the entire population.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 includes a flow chart representation of a variant of an embodiment of the method;

FIG. 2 includes a representation of a variation of an embodiment of the method and system;

FIG3 includes a variation of a process for generating a characterization model in an embodiment of the method;

FIG. 4 includes variations on the mechanisms by which probiotic therapy operates in embodiments of the methods;

FIG5 includes variations in sample handling in embodiments of the method;

FIG6 includes an embodiment of notification provision;

FIG. 7 includes a schematic representation of a variant of an embodiment of the method;

8A-8C include variations of performing the characterization process using a model;

FIG. 9 includes a variation of the method of promoting the treatment of the disease;

FIG10 includes variations of a microbiome characterization module;

FIG11 includes variations of a microbiome characterization module;

FIG12 includes variations of a microbiome characterization module;

FIG13 includes variations of a microbiome characterization module;

FIG14 includes variations of a microbiome characterization module;

FIG15 includes variations of a microbiome characterization module;

FIG16 includes variations of a microbiome characterization module;

FIG17 includes a variation of a multi-site analysis;

FIG. 18 includes a specific embodiment of a Venn Diagram having a comparison of results from different statistical techniques (e.g., univariate statistical techniques) for intestinal sampling sites;

FIG. 19 includes a specific example of a representation of dimensionality reduction obtained from the application of analysis module B, with each microbiome subsystem detected as represented by a different grayscale color and the associated modules represented by solid black lines;

FIG20 includes a specific embodiment of a representation of the interaction between microbial classification criteria and functions, with functions represented by squares and classification criteria represented by circles;

Figure 21 includes specific examples of variation explained by microbiome signatures associated with each condition analyzed, with values corresponding to the mean and 32nd and 68th percentiles of variation explained, and conditions organized on each panel by primary manifestation site.

FIG. 22 includes a particular embodiment of a representation of a cluster analysis using microbiome-based significance correlations to obtain a data-driven arrangement of the conditions being analyzed;

FIG. 23 includes variations of microbiome characterization modules and associated aspects;

FIG. 24 includes a specific embodiment of a heat map of microbiome-related associations among microbe-related conditions; and

FIG. 25 includes specific examples showing the number of individuals with comorbidity within and between clusters.

DETAILED DESCRIPTION

The following description of the embodiments is not intended to limit the embodiments, but rather to enable any person skilled in the art to make and use them.

1. Overview

As shown in FIG. 1 , an embodiment of a method 100 for characterizing one or more microorganism-related conditions (e.g., disease-related conditions, human behavior conditions, etc.) may include: determining a microbial dataset associated with a subject set (e.g., a microbial sequence dataset, a microbiome composition diversity dataset such as one based on a microbial sequence dataset, a microbiome functional diversity dataset such as one based on a microbial sequence dataset, etc.) S110; and using a microbiome characterization module set to apply analysis techniques based on the microbial dataset (e.g., based on microbiome features derived from the microbial dataset, etc.) to perform a characterization process (e.g., preprocessing, feature generation, feature processing, multi-site characterization for multiple collection sites, cross-condition analysis for multiple microbiome-related conditions, model generation, etc.) S130.

Embodiments of method 100 may additionally or alternatively include one or more of the following: processing a supplemental data set (e.g., describing one or more characteristics of a user, such as a medical condition history) associated with (e.g., providing useful information about; describing; representing; associated with, etc.) one or more microbial-related conditions of the subject set; history) etc.) S120; determining a therapy model for determining a therapy for preventing, ameliorating and/or otherwise altering one or more microbial-related conditions S140; processing one or more biological samples associated with a user (e.g., a subject, a human, an animal, a patient, etc.) S150; using a characterization process, determining a microbial-related characterization (e.g., a human behavior characterization, a disease-related characterization, etc.) for the user based on processing a user microbial dataset (e.g., a user microbial sequence dataset, a user microbiome composition dataset, a user microbiome function dataset, etc.) derived from the user's biological sample S160; facilitating therapeutic intervention for one or more microbial-related conditions of the user (e.g., based on a microbial-related condition and/or therapy model; etc.) S170; monitoring the effectiveness of a therapy for the user based on processing the biological sample to evaluate the microbiome composition and/or functional characteristics associated with the therapy for the user over time S180; and/or any other suitable operation.

Embodiments of method 100 and/or system 200 may function to apply one or more microbiome characterization modules (e.g., for applying one or more analytical techniques, etc.) to characterize (e.g., assess, evaluate, diagnose, describe, etc.) microbiome-related conditions and/or users associated with microbiome-related conditions (e.g., human behavior conditions, disease-related conditions, etc.), such as for facilitating therapeutic interventions (e.g., therapy selection; therapy promotion and/or provision; therapy monitoring; therapy evaluation; etc.). In one embodiment, method 100 may include: determining a microbial sequence dataset associated with the subject set based on microbial nucleic acids from biological samples associated with the subject set, wherein the microbial nucleic acids are associated with the microbial-related condition; using a microbiome characterization module set, applying an analysis technique set (e.g., at least one statistical test, such as a univariate statistical test, a dimensionality reduction technique, an artificial intelligence method, other methods described herein, etc.) based on the microbial sequence dataset to determine a microbiome feature set; generating a microbiome-related condition model based on the microbiome feature set (and/or any other suitable data) (e.g., for phenotypic prediction, such as estimating a user's propensity score for a microbiome-related condition, etc.); and determining a characterization of a microbiome-related condition for a user based on the microbiome-related condition model and a sample from the user (e.g., generating a user microbiome feature value through sample processing and computational processing for use with the microbiome-related condition model, etc.).

Additionally or alternatively, embodiments of method 100 and/or system 200 can function to perform cross-condition analysis (e.g., using one or more microbiome characterization modules, etc.) for multiple microbial-related conditions (e.g., characterization of multiple microbial-related conditions, etc.), such as in the context of characterizing, diagnosing and/or treating a user. In one embodiment, method 100 may include: determining a microbial sequence dataset associated with a subject set based on microbial nucleic acids from biological samples associated with a subject set, wherein the microbial nucleic acids are associated with multiple microbial-related conditions (e.g., the microbial nucleic acids are associated with microbial group features, etc., and the microbial group features are associated with two or more of the multiple microbial-related conditions); using a microbial group characterization module set, determining a multi-conditional microbial group feature set based on the microbial sequence dataset, wherein each multi-conditional microbial group feature in the multi-conditional microbial group feature set is associated with at least two microbial-related conditions in the multiple microbial-related conditions (e.g., features shared in multiple microbial-related conditions regarding relevance, correlation, covariance, etc.); determining a multi-conditional characterization of microbial-related conditions (e.g., a subset, all, etc.) in multiple microbial-related conditions for a user based on the multi-conditional microbial group feature set and a sample from the user; and facilitating therapeutic intervention for a microbial-related condition in the multiple microbial-related conditions based on the multi-conditional characterization.

Additionally or alternatively, embodiments of method 100 and/or system 200 may identify microbiome signatures associated with different microbiome-related conditions, such as for use as biomarkers (e.g., for diagnostic procedures, for therapeutic procedures, etc.). In embodiments, the microbiome-related signatures may be associated with at least one or more of a user's microbiome composition (e.g., microbiome composition diversity, etc.), microbiome function (e.g., microbiome functional diversity, etc.), and/or other suitable microbiome-related aspects.

Additionally or alternatively, embodiments may function to promote therapeutic interventions for microbial-related conditions, such as through promotion of associated therapies (e.g., with respect to specific physiological sites: intestinal tract, skin, nose, mouth, genitals, other suitable physiological sites, other collection sites, etc.). Additionally or alternatively, embodiments may function to generate models (e.g., such as microbiome characterization modules for phenotypic prediction and/or prediction scores, such as machine learning models for feature processing, etc.), such as models that can be used to characterize and/or diagnose users based on their microbiome (e.g., user microbiome features; as clinical diagnosis; as companion diagnostics, etc.), and/or can be used to select and/or provide therapies for subjects regarding one or more microbial-related conditions (e.g., probiotic-based therapeutic measures, phage-based therapeutic measures, small molecule-based therapeutic measures, clinical measures, etc.). Additionally or alternatively, embodiments may perform any suitable functions described herein.

Thus, data from a population of subjects (e.g., associated with one or more microbial-related conditions, etc.) can be processed using one or more microbiome characterization modules (e.g., for generating models, etc.) to characterize subsequent users, such as for indicating microbial-related health states and/or areas for improvement, and/or to facilitate therapeutic intervention (e.g., promoting one or more therapies; facilitating the adjustment of the composition and/or functional diversity of a user's microbiome toward one or more desired sets of equilibrium states, such as states associated with improved health states, which are associated with one or more microbial-related conditions, etc.). Variations of method 100 can also facilitate the selection, monitoring (e.g., efficacy monitoring, etc.) and/or adjustment of therapies provided to users, such as by collecting and analyzing additional samples from subjects for one or more microbial-related conditions, over time (throughout the course of a treatment regimen, by the user's experience with microbial-related conditions; etc.) and/or across collection sites (e.g., using microbiome characterization modules) (e.g., where characterization can include cross-condition characterization for multiple conditions, etc.). However, any suitable portion of method 100 and/or system 200 can use data from a population, subpopulation, individual, and/or other suitable entity for any suitable purpose.

Embodiments of method 100 and/or system 200 may preferably generate and/or promote (e.g., provide; present; notify regarding, etc.) representations and/or therapies for one or more microbial-related conditions, which may include one or more of the following: diseases, symptoms, causes (e.g., predispositions, etc.), disorders, associated risks (e.g., propensity scores, etc.), associated severity, behaviors (e.g., caffeine consumption, habits, diet, etc.), and/or any other suitable aspect associated with a microbial-related condition. Microbiome-related conditions may include one or more disease-related conditions, which may include any one or more of the following: skin-related conditions (e.g., acne, dermatomyositis, eczema, rosacea, dry skin, psoriasis, dandruff, photosensitivity, rough skin, itching, flaking, scaling, peeling, fine lines or cracks, graying of the skin in dark-skinned individuals, redness, deep cracks such as fissures that can bleed and lead to infection, itching and scaling of the scalp skin, oily skin such as irritating oily skin, sensitivity of the skin to products such as hair care products, imbalance of the scalp microbiome, etc.); gastrointestinal tract-related conditions (e.g., irritable bowel syndrome, inflammatory bowel disease, ulcerative colitis, celiac disease, Crohn's disease, bloating, hemorrhoids, constipation, reflux, bloody stools, diarrhea); Allergy-related conditions (e.g., allergies and/or intolerances related to wheat, gluten, dairy, soy, peanuts, shellfish, tree nuts, eggs, etc.); sports-related conditions (e.g., gout, rheumatoid arthritis, osteoarthritis, reactive arthritis, multiple sclerosis, Parkinson's disease, etc.); cancer-related conditions (e.g., lymphoma, leukemia, blastoma, germ cell tumor, carcinoma, sarcoma, breast cancer, prostate cancer, basal cell carcinoma, skin cancer, colon cancer, lung cancer, cancer associated with any appropriate physiological area, etc.); cardiovascular-related conditions (e.g., coronary heart disease, inflammatory heart disease, valvular heart disease, obesity, stroke, etc.); anemia conditions (e.g., thalassemia, sickle cell, malignant, Fanconi, hemolytic, aplastic, iron deficiency, etc.); neurological-related conditions (e.g., attention deficit hyperactivity disorder, ... hyperactivity disorder (ADHD), attention deficit disorder (ADD), anxiety, Asperger's syndrome, autism, chronic fatigue syndrome, depression, etc.); autoimmune-related conditions (e.g., Sprue, AIDS, Sjogren's, systemic lupus erythematosus, etc.); endocrine-related conditions (e.g., obesity, Graves' disease, Hashimoto's thyroiditis, metabolic diseases, type I diabetes, type II diabetes, etc.); Lyme disease conditions; communication-related conditions; sleep-related conditions; metabolic-related conditions; weight-related conditions; pain-related conditions; genetic-related conditions; chronic diseases and/or any other suitable type of disease-related conditions. In variations, method 100 and/or system 200 embodiments may be used in promoting (e.g., providing, etc.) one or more targeted therapies to users suffering from one or more microbial-related conditions (e.g., skin-related conditions, etc.). Additionally or alternatively, microbe-related conditions may include one or more human behavior conditions, which may include any one or more of the following: caffeine consumption, alcohol consumption, other food consumption, dietary supplement consumption, probiotic-related behavior (e.g., consumption, avoidance, etc.), other dietary behaviors, habitual behaviors (e.g., smoking, exercise conditions such as low, moderate and/or extreme exercise conditions, etc.), menopause, other biological processes, social behavior, other behaviors, and/or any other suitable human behavior conditions. Conditions may be associated with any suitable phenotype (e.g., a phenotype measurable for a human, animal, plant, fungal organism, etc.).

Embodiments of method 100 and/or system 200 may be implemented for a single user, such as with respect to applying one or more microbiome characterization modules for processing one or more biological samples from a user (e.g., collected across one or more collection sites) for microbiome-related characterization, facilitating therapeutic intervention, and/or for any other suitable purpose (e.g., for one or more microbiome-related conditions, etc.). Additionally or alternatively, embodiments may be implemented for a subject population (e.g., including the user, excluding the user), wherein the subject population may include subjects that are similar and/or dissimilar to other subjects of any suitable type of characteristics (e.g., with respect to microbiome-related conditions, demographic characteristics, behavior, microbiome composition and/or function, etc.); for a subgroup of users (e.g., shared characteristics, such as characteristics that affect microbiome-related characteristics and/or therapy determination, etc.); for plants, animals, microorganisms, and/or any other suitable entity. Thus, information derived from a subject collection (e.g., a subject population, a subject collection, a subgroup of users, etc.) may be used to provide additional insights to subsequent users. In variations, aggregate sets of biological samples are preferably associated with and processed for various users, such as users including one or more of: different demographics (e.g., gender, age, marital status, race, nationality, socioeconomic status, sexual orientation, etc.), different microbiome-related conditions (e.g., health and disease states, different genetic dispositions; etc.), different living situations (e.g., living alone, living with pets, living with significant others, living with children, etc.), different dietary habits (e.g., omnivorous, vegetarian, vegan, sugar consumption, acid consumption, caffeine consumption, etc.), different behavioral tendencies (e.g., physical activity level, medication use, alcohol consumption, etc.), different mobility levels (e.g., regarding distance traveled in a given time period), and/or any other suitable characteristics (e.g., characteristics that affect, are associated with, and/or are otherwise associated with microbiome composition and/or function, etc.). In an embodiment, as the number of users increases, the process prediction capabilities implemented in portions of method 100 may increase, such as with respect to characterizing various users based on their microbiome (e.g., with respect to different collection sites for users' samples, etc.). However, portions of method 100 and/or system 200 may be performed and/or configured in any suitable manner for any suitable entity or entities.

The data described herein (e.g., microbiome characterization module inputs, microbiome characterization module outputs, microbiome datasets, microbiome features, microbiome-related features, therapy-related data, user data, supplemental data, notifications, etc.) can be associated with any suitable temporal indicators (e.g., seconds, minutes, hours, days, weeks, etc.), including one or more of the following: temporal indicators indicating when the data was collected (e.g., temporal indicators indicating when a sample was collected; etc.), determined, transmitted, received, and/or otherwise processed; temporal indicators that provide context to the content described by the data; (e.g., temporal indicators associated with microbiome-related features, such as where the microbiome-related features describe a microbiome-related condition and/or a user's microbiome status at a particular time, etc.); changes in temporal indicators (e.g., changes in microbiome-related features over time, such as in response to receiving a therapy; latency between sample collection, sample analysis, providing a microbiome-related feature or therapy to a user, and/or other suitable parts of method 100, etc.); and/or any other suitable time-related indicators.

Additionally or alternatively, parameters, metrics, inputs, outputs, and/or other suitable data can be associated with value types, including: scores (e.g., microbial-related condition propensity scores; feature correlation scores; association scores, covariance scores, microbiome diversity scores, severity scores, etc.), individual values (e.g., individual microbial-related scores for different collection sites, such as condition propensity scores, etc.), summary values (e.g., overall scores based on individual microbial-related scores for different collection sites, etc.), binary values (e.g., microbiome feature presence or absence; microbial The present invention also provides a method for determining the presence or absence of a microbial-related condition, such as the presence or absence of a microbial-related condition, etc.), a relative value (e.g., relative taxonomic group abundance, relative microbiome functional abundance, relative feature abundance, etc.), a classification (e.g., classification and/or diagnosis of a microbial-related condition for a user, classification of a microbial-related condition cluster for a condition; feature classification; behavior classification; demographic classification, etc.), a confidence level (e.g., associated with a microbial sequence data set; associated with a microbiome diversity score; associated with other microbial-related characterizations; associated with other outputs, etc.), an identifier (e.g., identifying a microbiome characterization module used in processing data, etc.), a value on a spectrum, and/or any other suitable type of value. Any suitable type of data described herein can be used as input (e.g., for different modules, models, and/or other suitable components described herein), generated as output (e.g., output of different models, modules, etc.), and/or operated in any suitable manner for any suitable component associated with method 100 and/or system 200.

One or more instances and/or portions of the method 100 and/or processes described herein can be performed asynchronously (e.g., sequentially), simultaneously (e.g., parallel data processing with a microbiome characterization module; simultaneous cross-condition analysis; multiple sample processing, such as multiple amplification of microbial nucleic acid fragments corresponding to target sequences associated with microbial-related conditions; performing sample processing and analysis for substantially simultaneous evaluation of a panel of microbial-related conditions; computationally determining microbial datasets, microbiome signatures, and/or characterizing microbial-related conditions in parallel for multiple users; such as simultaneously on different threads to parallelize computations to increase system processing capabilities, etc.), in time relation to a triggering event (e.g., execution of a portion of the method 100) (e.g., substantially simultaneously, in response to, continuously, prior to, subsequent to, etc.), and/or in any other suitable order, at any suitable time and frequency, by and/or using one or more instances of the system 200, components, and/or entities described herein. In one embodiment, method 100 may include: generating a microbial data set based on processing microbial nucleic acids of one or more biological samples using a bridge amplification substrate of a next generation sequencing platform (and/or other suitable sequencing system) of a sample processing system, and determining microbiome features and microbiome functional diversity features in a computing device operable to communicate with the next generation sequencing platform. However, method 100 and/or system 200 may be configured in any suitable manner.

2. Benefits

Microbiome analysis can enable accurate and/or effective characterization, and/or provision of therapy for microbe-related conditions caused by and/or otherwise associated with microbes (e.g., according to portions of method 100, etc.). Certain embodiments of the technology can overcome several challenges faced by traditional methods in characterizing user conditions (e.g., microbe-related conditions) and/or facilitating therapeutic interventions. First, traditional methods may require patients to visit one or more care providers to receive characterization and/or therapy recommendations for microbe-related conditions (e.g., through diagnostic medical procedures such as blood tests; etc.), which may amount to inefficiencies and/or health risks associated with the amount of time that elapses before diagnosis and/or treatment, inconsistencies in the quality of medical care, and/or other aspects of care provider visits. Second, conventional gene sequencing and analysis techniques used for human genome sequencing may be incompatible and/or inefficient when applied to microbiomes (e.g., where the human microbiome may include more than 10 times the number of microbial cells as human cells; where feasible analysis techniques and means of utilizing analysis techniques may be different; where optimal sample processing techniques may be different, such as for reducing amplification bias; where different approaches to characterizing microorganisms may be employed; where the types of conditions and associations may be different; where the causes of associated conditions and/or feasible therapies for associated conditions may be different; where sequence reference databases may be different; where the microbiomes may vary across different body regions of the user, such as at different collection sites; etc.). Third, the rise of sequencing technologies (e.g., next generation sequencing, associated technologies, etc.) has raised technical issues (e.g., issues with data processing and analysis of large amounts of generated sequence data; issues with processing multiple biological samples in a multiplexed manner; information display issues; therapy prediction issues; therapy delivery issues, etc.), but these technical issues will not exist due to the unprecedented developments in speed and data generation associated with sequencing genetic material. Certain embodiments of the method 100 and/or system 200 may provide technologically-rooted solutions to at least the challenges discussed above.

First, a specific embodiment of the technology can transform an entity (e.g., a user, a biological sample, a therapy-facilitating system including a medical device, etc.) into a different state or thing. For example, the technology can transform a biological sample into a component that can be sequenced and analyzed to generate a microbial data set and/or a microbiome signature that can be used to characterize a user with respect to one or more microbial-related conditions (e.g., such as by using a microbiome characterization module, a next-generation sequencing system, a multiplex amplification operation, etc.). In another embodiment, the technology can identify, promote (e.g., propose, suggest), discourage (discouraging) and/or provide therapy (e.g., personalized therapy based on microbiome characterization, etc.) and/or otherwise promote therapeutic intervention (e.g., promote changes in the user's microbiome composition, microbiome function, etc.), which can prevent and/or improve one or more microbial-related conditions, thereby transforming the patient's microbiome and/or health (e.g., improving health status associated with microbial-related conditions; etc.). In another embodiment, the technology can transform the microbiome composition and/or function at one or more different physiological sites of the user (e.g., one or more different collection sites, etc.), such as targeting and/or transforming microorganisms associated with the intestinal, nasal, skin, oral and/or genital microbiomes. In another embodiment, the technology can control a therapy-related system (e.g., a dietary system; an automatic medication dispenser; a behavior change system; a diagnostic system; a disease therapy promotion system, etc.) to promote a therapy (e.g., by generating control instructions for execution for a therapy promotion system, etc.), thereby transforming the therapy promotion system.

Second, specific embodiments of the technology can confer improvements in computer-related technologies (e.g., improving computational efficiency in storing, retrieving, and/or processing microbial-related data for microbial-related conditions; computational processing associated with biological sample processing, etc.), such as by facilitating computers to perform non-previously executable functions. For example, the technology can utilize a collection of microbiome characterization modules to apply a variety of analytical techniques to non-universal microbial datasets and/or microbiome features in a non-universal manner (e.g., the microbial datasets and/or microbiome features are recently generated and/or feasible due to advances in sample processing technology and/or sequencing technology, etc.), to improve microbial-related characterizations and/or facilitate therapeutic interventions for microbial-related conditions.

Third, specific embodiments of the technology can confer improvements in processing speed, microbe-related characterization, accuracy, microbiome-related therapy determination and promotion, and/or other suitable aspects of microbe-related conditions. For example, the technology can utilize a set of microbiome characterization modules with a non-universal microbial data set to determine, select and/or otherwise process microbiome features that are particularly relevant to one or more microbe-related conditions (e.g., processed microbiome features associated with correlation scores for microbe-related conditions; cross-conditional microbiome features associated with multiple microbe-related conditions, etc.), which can promote accuracy (e.g., by using the most relevant microbiome features; by utilizing customized analysis techniques, etc.), processing speed (e.g., by selecting a subset of relevant microbiome features; by performing dimensionality reduction techniques; by utilizing customized analysis techniques, etc.), and/or other computational improvements with respect to phenotypic prediction (e.g., indication of microbe-related conditions, etc.), other suitable characterizations, promotion of therapeutic interventions, and/or other suitable purposes. In certain embodiments, the technology can apply feature selection rules (e.g., microbiome feature selection rules for composition, function; microbiome feature selection rules for supplementary features extracted from supplementary data sets, etc.) together with one or more microbiome characterization modules to select an optimized subset of features (e.g., microbiome functional features associated with one or more microbial-associated conditions; microbiome composition diversity features, such as reference relative abundance features representing healthy, present, absent, and/or other suitable ranges of taxonomic groups associated with microbial-associated conditions; user relative abundance features that can be compared to reference relative abundance features associated with microbial-associated conditions and/or therapy responses; etc.) from a large potential pool of features (e.g., extractable from a plethora of microbiome data such as sequence data; identifiable by statistical tests such as univariate statistical tests; etc.) for generating, applying, and/or otherwise facilitating characterization and/or therapy (e.g., through models, etc.). The potential size of microbiomes (e.g., human microbiomes, animal microbiomes, etc.) can translate into large amounts of data, giving rise to the problem of how to process and analyze large amounts of data to generate actionable microbiome insights about microbial-associated conditions. However, feature selection rules and/or other suitable computer-implementable rules can enable one or more of the following: shorter generation and execution time (e.g., for generating and/or applying models; for determining microorganism-related characteristics and/or associated therapies; etc.); optimized sample processing techniques (e.g., such as in the case of optimizing to improve specificity, reduce amplification bias and/or other suitable parameters, by using primer types, other biomolecules and/or other sample processing components identified by taxonomic groups, sequences and/or other suitable data related to microorganism-related conditions, improving the conversion of microbial nucleic acids from biological samples; etc.); model simplification that facilitates efficient interpretation of results; reduction of overfitting; generation, storage and application of microbiome tables related to microorganism-related conditions over time for multiple users; and/or other suitable improvements in the art.

Fourth, certain embodiments of the technology can amount to an inventive distribution of functionality across a network including a sample processing system, a microbe-related characterization system (e.g., including a collection of microbiome characterization modules, each of which can have different but complementary functions, etc.), and multiple users, wherein the sample processing system can handle substantially simultaneous processing of biological samples from multiple users (e.g., in a multiplexed manner), which can be utilized by the microbe-related characterization system to generate personalized characterizations and/or treatments for microbe-related conditions (e.g., customized to a user's microbiome, such as related to the user's dietary behavior, probiotic-related behavior, medical history, demographics, other behaviors, preferences, etc.).

Fifth, specific embodiments of the technology can improve at least the technical field of genomics, microbiology, microbiome-related computing, diagnosis, therapy, microbiome-related digital medicine, general digital medicine, modeling and/or other related fields. In one embodiment, the technology can use a set of microbiome characterization modules to model and/or characterize different microbial-related conditions, such as by calculating the identification of relevant microbial features for microbial-related conditions (e.g., it can serve as a biomarker to be used in diagnosis, promote therapeutic intervention, etc.). In another embodiment, the technology can perform cross-condition analysis to identify and evaluate cross-condition microbiome features, which are related to multiple microbial-related conditions (e.g., diseases, phenotypes, etc.) (e.g., shared across multiple microbial-related conditions, associated across multiple microbial-related conditions, etc.). The identification and characterization of the microbiome features can promote the practice of improved health care (e.g., at the population and individual level, such as by promoting diagnostic and therapeutic intervention, etc.) by reducing the risk and prevalence of comorbid and/or multi-disease microbial-related conditions (e.g., it can be associated with environmental factors, thereby associated with the microbiome, etc.).

Sixth, the technology can utilize dedicated computing devices (e.g., systems associated with sample processing systems such as next-generation sequencing systems; systems associated with microbial characterization systems; systems associated with therapy enhancement systems, etc.) to perform appropriate portions associated with method 100 and/or system 200.

However, particular embodiments of the technology may provide any other suitable benefits where a non-general purpose computer system is used for microbe-related characterization, microbiome modulation, and/or for performing other suitable portions of method 100 .

3. System

As shown in FIG. 2 , an embodiment of a system 200 (e.g., for characterizing a microbial-related condition) may include any one or more of the following: a processing system (e.g., a sample processing system, etc.) 210 operable to collect and/or process biological samples (e.g., collected by a user and included in a container that includes a pretreatment reagent; etc.) from one or more users (e.g., a human subject, a patient, an animal subject, an environmental ecosystem, a care provider, etc.) to determine a microbial dataset (e.g., a microbial genetic sequence; a microbial sequence dataset, etc.); a microbial-related characterization system 220 operable to determine a user's microbiome characteristics (e.g., a microbiome composition characteristic; a microbiome functional characteristic; a diversity characteristic; a relative abundance range; such as based on a microbial dataset and/or other suitable data; etc.), determine a microbial-related characterization (e.g., a microbial-related condition characterization, a therapy-related characterization, a characterization for a user, etc.); and/or a therapy promotion system 230 operable to promote therapeutic intervention (e.g., promoting therapy, etc.) for one or more microbial-related conditions (e.g., based on one or more microbial-related conditions; etc.).

In certain embodiments, the system 200 may include a sample processing system, the sample processing system including: a sequencing system (e.g., a next generation sequencing system, etc.), the sequencing system operable to determine a microbial genetic sequence based on a biological sample associated with a subject set, wherein the biological sample includes a microbial nucleic acid associated with a microbial-related condition; a set of microbiome characterization modules 221, which are operable to apply a set of analysis techniques, the analysis techniques including at least two of the following: statistical tests (e.g., univariate statistical tests, etc.), dimensionality reduction techniques, artificial intelligence methods, and/or other suitable methods described herein, and wherein the set of microbiome characterization modules 221 includes: a first microbiome characterization module 221', which is operable to apply a first analysis technique in the set of analysis techniques (e.g., one or more univariate statistical tests and/or suitable statistical tests, etc.) to determine a microbiome feature set based on the microbial genetic sequence, wherein the microbiome feature set is associated with a microbial-related condition (e.g., associated with a microbial-related condition, etc.); and a second microbiome characterization module 221'. A microbiome characterization module 221', which is operable to apply a second analysis technique (e.g., a dimensionality reduction technique) in the set of analysis techniques to determine a processed microbiome feature set (e.g., a reduced feature set; a feature set including the most relevant features for one or more microbiome-related conditions, etc.) based on a microbiome feature set (e.g., wherein the output of the first microbiome characterization module 221' can be used as an input to the second microbiome characterization module 221' in a serial, chained manner, etc.), wherein the processed microbiome feature set is suitable for improving the characterization of microbiome-related conditions (e.g., by identifying and utilizing a subset of customized features from a huge pool of potential features to improve accuracy, processing speed, thereby improving the functionality of a computing system regarding microbiome-related characterization, promotion of therapeutic intervention, and/or other suitable functions described herein, etc.); and a microbiome-related condition model generated based on the processed microbiome feature set, wherein the microbiome-related condition model is operable to determine a characterization of a microbiome-related condition for a user.

The processing system 210 of the system 200 can function to receive and/or process (e.g., fragment, amplify, sequence, generate relevant data sets, etc.) biological samples to convert microbial nucleic acids and/or other components of the biological samples into data (e.g., genetic sequences that can be subsequently aligned and analyzed; microbial data sets, etc.) to facilitate the generation of relevant characterization and/or therapeutic interventions of microorganisms. The processing system 210 can additionally or alternatively function to provide a sample kit 250 (e.g., including a sample container, instructions for collecting samples from one or more collection sites, etc.) to multiple users (e.g., in response to a purchase order for the sample kit 250), such as through a postal system. The processing system 210 may include one or more sequencing systems 215 (e.g., next generation sequencing systems, sequencing systems for targeted amplicon sequencing, metatranscriptomic sequencing, metagenomic sequencing, sequencing-by-synthesis technology, capillary sequencing technology, Sanger sequencing, pyrosequencing technology, nanopore sequencing technology, etc.) for sequencing biological samples (e.g., sequencing microbial nucleic acids from biological samples), such as in generating microbial data (e.g., microbial sequence data, other data for microbial data sets, etc.). The processing system 210 may additionally or alternatively include a library preparation system that is operable to automatically prepare biological samples to be sequenced by the sequencing system in a multiplexed manner (e.g., fragmentation and amplification using primers compatible with a genetic target, the genetic target being associated with a microbial-related condition); and/or any suitable components. The processing system may perform any suitable sample processing technique described herein. However, the processing system 210 and associated components may be configured in any suitable manner.

The microbiome characterization system 220 of the system 200 can function to determine, analyze, characterize, and/or otherwise process microbial data sets (e.g., biological samples based on processing that results in microbial genetic sequences; comparisons with reference sequences; etc.), microbiome features (e.g., individual variables; sets of variables; features associated with phenotypic prediction, statistical descriptions; variables associated with samples obtained from individuals; variables associated with microbial-related conditions; variables that fully or partially describe the microbiome composition and/or function of a sample in relative or absolute quantities, etc.), models (e.g., microbial-related condition models, etc.), and/or other suitable data to facilitate microbial-related characterization and/or therapeutic intervention. In an embodiment, the microbiome characterization system 220 can identify feature information derived from statistical descriptions of differences between samples associated with one or more microbial-related conditions (e.g., samples associated with the presence, absence, risk, tendency, and/or other aspects of microbial-related conditions, etc.), such as where different analyses can provide complementary views into features that distinguish different samples (e.g., distinguishing subgroups associated with the presence or absence of a condition, etc.). In a particular embodiment, individual predictors, specific biological processes, and/or latent variables for statistical inference can provide supplementary information at different data complexity levels to facilitate downstream opportunities for changes in characterization, diagnosis, and/or treatment. In a particular embodiment, the microbiome characterization system 220 can generate and/or apply a therapy model (e.g., based on cross-condition analysis, etc.) to identify and/or characterize therapies for treating one or more microbial-related conditions. In another particular embodiment, the microbiome characterization system 220 processes supplementary data (e.g., prior knowledge to be used to improve the application of the microbiome characterization module 221; such as prior knowledge associated with users, microbiome features, microbial-related conditions, other components, etc.).

The microbiome characterization system 220 preferably includes one or more microbiome characterization modules 221 (e.g., independent modules, interdependent modules, etc.), which can be used to apply one or more analytical techniques to process microbial datasets, microbiome signatures, supplemental data and/or other suitable data to facilitate microbiome-related characterization and/or therapeutic intervention (e.g., as shown in FIG. 23 ).

Any suitable microbiome characterization module 221 (e.g., using any suitable analysis technique, etc.) can be applied in any suitable manner, in series (e.g., by linking microbiome characterization modules 221 with respect to outputs and inputs), simultaneously, repeatedly, and/or in any suitable temporal relationship, in any suitable combination. For example, the output of a microbiome characterization module 221 can constitute a microbiome-related characterization (e.g., a result of interest itself, etc.), which is considered an intermediate component (e.g., used as an input to the same or different microbiome characterization module 221, an input to a model such as a therapy model, etc.), and/or is used for any suitable purpose. In certain embodiments, multiple microbiome characterization modules 221 can be linked (e.g., such as where one or more outputs of a microbiome characterization module 221 can be used as one or more inputs to the same or another microbiome characterization module 221, etc.) and/or otherwise connected (e.g., with respect to data sharing, with respect to contributing to microbiome-related characterization, with respect to association with one or more microbiome-related conditions, etc.), which can facilitate one or more feature selections (e.g., selecting a subset of microbiome features for subsequent use, etc.), feature weighting (feature weighting, etc.), and/or other features. weighting) (e.g., for determining different weights for different features, such as up-weighting or down-weighting features, which can be used in any suitable microbiome characterization module 221, model and/or other suitable process, etc.), hot start (e.g., where the output and/or other processing associated with the first microbiome characterization module 221' can assist and/or improve the processing associated with the second microbiome characterization module 221", such as with respect to improving statistical learning and/or inference, helping and/or otherwise improving with, which can be related to promoting focus on the most relevant features, etc.). For example, the first microbiome characterization module 221' can determine a microbiome feature set (e.g., by applying a first analysis technique); the second microbiome characterization module 221" can apply (e.g., be operable to apply) a second analysis technique to perform at least one of feature selection, feature weighting, and hot start to process the microbiome feature set into a processed microbiome feature set. However, the microbiome characterization module 221 can be applied for any suitable purpose, at any suitable time and frequency, for any number of data sets, users, microbiome-related conditions, therapies, and/or other suitable entities.

Different microbiome characterization modules 221 (e.g., different combinations of microbiome characterization modules 221; different modules applying different analysis techniques; different input and/or output types; applied in different ways such as with respect to time and/or frequency; etc.) can be based on one or more of the following applications (e.g., executed, selected, retrieved, stored, etc.): microbiome-related conditions (e.g., using different combinations of microbiome characterization modules 221 depending on one or more microbiome-related conditions being characterized, such as where different microbiome characterization modules 221 have different levels of suitability for processing data about different microbiome-related conditions, etc.), users (e.g., different microbiome characterization modules 221 based on different user data and/or characteristics, such as corresponding sample collection sites, demographics, genetics, environmental factors, etc.), microbiome-related characterizations (e.g., different microbiome characterization modules 221 for different types of characterizations, such as therapy-related characterizations versus diagnosis-related characterizations, such as for identifying relevant microbiome compositions relative to propensity scores for determining microbiome-related conditions; etc.), therapies (e.g., different microbiome characterization modules 221 for monitoring the efficacy of different therapies, etc.), and/or any other suitable components. In an embodiment, different microbiome characterization modules 221 can be customized for different types of inputs, outputs, microbe-related characterizations, microbe-related conditions (e.g., measurements of different phenotypes that need to be characterized), and/or any other suitable entities. However, the microbiome characterization modules 221 can be customized and/or used in any suitable manner to facilitate microbe-related characterizations and/or therapeutic interventions.

The microbiome characterization module 221 of the system 200, the model, other components and/or appropriate parts of the method 100 (e.g., determining microbiome features, determining microbiome-related features, etc.) can use any one or more of the following analysis techniques: statistical tests (e.g., univariate statistical tests, multivariate statistical tests, etc.), dimensionality reduction techniques, artificial intelligence methods (e.g., machine learning methods, etc.), performing pattern recognition on data (e.g., identifying the association between microbiome-related conditions and microbiome features, etc.), fusing data from multiple sources (e.g., based on microbiome data and/or supplementary data from multiple users associated with one or more microbiome-related conditions, such as generating a characterization model based on microbiome features extracted from the data, etc.), combining values (e.g., average values, etc.), compression, conversion (e.g., digital-to-analog conversion, analog-to-digital conversion), performing statistical estimation on data (e.g., general least squares regression, non-negative least squares regression, principal component analysis, ridge regression, etc.), wave modulation, normalization, updating (e.g., updating of characterization models and/or therapy models based on treated biological samples over time; etc.), ranking (e.g., microbiome features; therapies; etc.), weighting (e.g., microbiome features, etc.), validation, filtering (e.g., for baseline correction, data clipping, etc.), noise reduction, smoothing, filling (e.g., gap filling), alignment, model fitting, binning, windowing, clipping, transformation, mathematical operations (e.g., derivatives, moving averages, summation, subtraction, multiplication, division, etc.), data association, multiplexing, demultiplexing, interpolation, extrapolation, clustering, image processing techniques, other signal processing operations, other image processing operations, visualization, and/or any other suitable processing operation. Artificial intelligence methods may include any one or more of the following: supervised learning (e.g., using logistic regression, using back-propagation neural networks, using random forests, decision trees, etc.), unsupervised learning (e.g., using an Apriori algorithm, using K-means cluster analysis), semi-supervised learning, deep learning algorithms (e.g., neural networks, restricted Boltzmann machines, deep belief network methods, convolutional neural network methods, recurrent neural network methods, stacked autoencoder methods, etc.), reinforcement learning (e.g., using a Q-learning algorithm, using temporal difference learning), regression algorithms (e.g., ordinary least squares, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, etc.), instance-based methods (e.g., k-nearest neighbors, learning vector quantization, self-organizing maps, etc.), regularization methods (e.g., ridge regression, least absolute shrinkage and selection operators, elastic nets, etc.), net), decision tree learning methods (e.g., classification and regression trees, iterative dichotomiser3, C4.5, chi-square automatic interaction detection, decision stumps, random forests, multivariate adaptive regression splines, gradient boosting machines, etc.), Bayesian methods (e.g., naive Bayes, averaged one-dependence estimator, etc.), estimatiors, Bayesian belief networks, etc.), kernel methods (e.g., support vector machines, radial basis functions, linear discriminant analysis, etc.), clustering analysis methods (e.g., k-means clustering analysis, expectation maximization, etc.), association rule learning algorithms (e.g., a priori algorithms, depth-first (Eclat) algorithms, etc.), artificial neural network models (e.g., perceptron methods, back-propagation methods, Hopfield network methods, self-organizing mapping methods, learning vector quantization methods, etc.), integration methods (e.g., enhancement, boostrapped aggregation, adaptive enhancement (AdaBoost), stacked generalization, gradient boosting machines, random forest methods, etc.) and/or any suitable artificial intelligence methods. However, data processing can be adopted in any suitable manner.

In a first variation, as shown in FIG10 , the microbiome characterization module 221 (e.g., analysis module A222) can apply one or more statistical tests (e.g., univariate statistical tests, multivariate, etc.), which can include any one or more of a t-test, a Kolmogorov-Smirnov test, a regression model, and/or other suitable techniques related to statistical tests. The microbiome characterization module 221 can apply statistical tests to determine a microbiome feature set (e.g., based on a microbial data set such as a microbial genetic sequence; based on prior knowledge, such as an association between microbiome features and microbial-related conditions, supplementary data representing useful information for subjects and users; etc.). The microbiome characterization module 221 can apply multiple statistical tests (e.g., univariate statistical tests, multivariate, etc.), which can complement each other by adopting different modeling strategies, such as for detecting changes in means and variances, or the presence and/or absence of patterns. In one embodiment, the outputs (e.g., results) of different types of statistical tests (e.g., univariate statistical tests, multivariate, etc.) (and/or other suitable analysis techniques) can be combined, grouped and/or otherwise aggregated to display associations (e.g., similarities, differences) between different analysis techniques (e.g., as shown in Figure 18, showing different single tests in Part A and Part C, and the union of outputs from multiple tests on Part B), such as with respect to identified microbiome features. In certain embodiments, the first microbiome characterization module 221 may apply (e.g., be operable to apply, etc.) a first statistical test (e.g., a univariate statistical test, etc.) to determine a first microbiome feature set, and the second microbiome characterization module 221" (and/or using the same first microbiome characterization module 221') may apply a second statistical test (e.g., a second univariate statistical test, etc.) to determine a second microbiome feature set. Aggregation from outputs of multiple analysis techniques may include an intersection or union between different outputs from different analysis techniques, wherein the outputs of these aggregations may be used to achieve a target balance of specificity and sensitivity (e.g., higher specificity with lower sensitivity; higher sensitivity with lower specificity, etc.). Any suitable microbial data set, input and/or output of a microbiome characterization module 221, and/or other suitable data may be used as an input, or may be used as an output of a statistical test, and the output of a microbiome characterization module 221 may be used as an input for any other suitable microbiome characterization module 221. However, the microbiome characterization module 221 (e.g., analysis module A222) may be configured in any suitable manner.

In a second variation, as shown in FIG11 , the microbiome characterization module 221 (e.g., analysis module B223) may apply one or more dimensionality reduction techniques, including any one or more of the following: supervised dimensionality reduction techniques; unsupervised dimensionality reduction techniques; missing value ratios; principal component analysis (PCA); probabilistic PCA; matrix decomposition techniques; compositional mixtures models such as latent Dirichlet distribution or hierarchical Dirichlet process; feature embedding techniques such as isomap or local linear embedding, local least squares regression, Sammon mapping, multidimensional scaling, projection pursuit; and/or any other suitable techniques related to dimensionality reduction. Applying dimensionality reduction techniques may reduce the number of dimensions (e.g., features, samples, etc.) in a data set. Any suitable microbial data set, input and/or output of the microbiome characterization module 221, and/or other suitable data can be used as input, or can be the output of a dimensionality reduction technique (e.g., using microbiome features determined by statistical tests as inputs to dimensionality reduction techniques to reduce the number of features, etc.), and the output of the microbiome characterization module 221 can be used as input to any other suitable microbiome characterization module 221 (e.g., for statistical tests; artificial intelligence methods such as random forests, kernel machines, support vector machines, regression methods; analysis modules A222; analysis modules C224, etc.). Applying the microbiome characterization module 221 can facilitate determining a linear or nonlinear association between inferred potential features and phenotype-related data, which is associated with one or more microbial-related conditions. The output of the microbiome characterization module 221 can include microbial-related characterizations (e.g., results of interest in themselves), outputs for additional analysis (e.g., by providing individual features with predictive value and/or potential features useful for clustering and classifying samples, etc.), and/or for any suitable purpose. However, the microbiome characterization module 221 (e.g., analysis module B223) can be configured in any suitable manner.

In a third variation, as shown in FIG12 , the microbiome characterization module 221 (e.g., analysis module C224) can facilitate the application of one or more machine learning models (and/or other suitable artificial intelligence methods). In an embodiment, the microbiome characterization module 221 can serve to guide the framework and/or parameter estimation of an artificial intelligence method (e.g., a neural network, an autoencoder model or a generative adversarial network, etc.), such as, for example, by guiding the nonlinear prediction function of encoding phenotypes and/or other microbial related conditions. Any suitable microbial data set, input and/or output of the microbiome characterization module 221, and/or other suitable data can be used as input, or can be output (e.g., using the output of a statistical test, the output of a dimensionality reduction method, the output of an analysis module A222, the output of an analysis module B223, and/or using any suitable data as input, etc.), and the output of the microbiome characterization module 221 can be used as the input of any other suitable microbiome characterization module 221. The output of the microbiome characterization module 221 may include microbiome-related characterizations (e.g., phenotypic predictions such as propensity scores for microbiome-related conditions, etc.), outputs for additional analysis (e.g., correlation scores for features describing predictive values, which can be used to identify features most relevant to phenotypic predictions and/or other types of predictions, etc.), and/or may be used for any suitable purpose. However, the microbiome characterization module 221 (e.g., analysis module C224) can be configured in any suitable manner.

In a fourth variation, as shown in FIG13 , the microbiome characterization module 221 (e.g., analysis module D225) may apply one or more analysis techniques (e.g., second-order or higher-order tests of interactions by regression and/or equivalent methods; machine learning algorithms such as random forests and/or support vector machines, data compression techniques; kernel machines, etc.) to detect statistical interactions between microbial data (e.g., different microbiome composition profiles, etc.), microbiome features, and/or features obtained from transformations of microbiome features (e.g., ratios, products, features obtained from the application of dimensionality reduction algorithms, etc.). However, the microbiome characterization module 221 (e.g., analysis module D225) may be configured in any suitable manner.

In a fifth variation, as shown in FIG. 14 , the microbiome characterization module 221 (e.g., analysis module E226) can determine phenotypic predictions, risk indices, propensity scores, other indices, and/or other suitable indicators associated with microbiome-related conditions (e.g., associated with diagnosing microbiome-related conditions for a user, etc.), such as by applying an analysis technique comprising at least one or more of the following: statistical tests (e.g., univariate statistical tests, multivariate statistical tests, etc.), univariate techniques, multivariate techniques, artificial intelligence methods (e.g., machine learning models, etc.), and/or other suitable techniques (e.g., where the output can be used as a summary of microbiome composition, function, and/or other suitable microbiome-related aspects associated with microbiome-related conditions). The microbiome characterization module 221 can define a minimum and/or maximum value for the output range, such as by using a standardized technique of empirical analyses. In one embodiment, a score can be calculated for a reference sample set (e.g., for data corresponding to a reference sample, etc.), where the minimum and maximum observed values can be recorded and used, thereby determining the minimum and maximum values of the output range. The scores for a particular sample (e.g., a subsequent sample) are normalized, which can facilitate scores in the range of 0 to 1. Additionally or alternatively, the microbiome characterization module 221 can determine a calibration score (e.g., with a recognizable value in characterization, diagnosis, and/or treatment guidance, etc.). In one embodiment, the microbiome characterization module 221 can determine a score (e.g., a propensity score, etc.) for a sample set (e.g., corresponding to healthy subjects and subjects with one or more microbial-related conditions of interest); convert the propensity score to a calibration score (e.g., in the range of 0 to 1) by calculating for each possible value of the propensity score (e.g., 10), the fraction of subjects with one or more microbial-related conditions of interest (e.g., # of diseased subjects/(# of diseased subjects + # of healthy subjects)), the score value is greater than or equal to the calibration score, wherein And where this can be viewed as estimating a probability density function of the fraction of diseased individuals as a function of the propensity score values. However, the microbiome characterization module 221 (eg, analysis module E 226 ) can be configured in any suitable manner.

In a sixth variation, as shown in FIG. 15 , a microbiome characterization module 221 (e.g., analysis module F226) may apply prior knowledge of microbiome features (e.g., associations between microbiome features and microbiome-related conditions, associations with user characteristics, etc.), microbiome-related conditions, users, microbiome data sets, and/or other suitable components (e.g., biological data, user data, etc.) to improve processing associated with other microbiome characterization modules 221 (e.g., analysis module A 222, analysis module B 223, analysis module C 224, etc.). In one embodiment, the microbiome characterization module 221 may guide statistical inference toward an improved prediction model with a lower error rate, thereby improving the functionality of the computing system. In an embodiment, the inclusion of this knowledge (e.g., prior information, etc.) may be performed by utilizing hard features, filtering, weighting schemes, including external variables in the data modeling step, other analysis techniques, and/or any other suitable process. However, the microbiome characterization module 221 (e.g., analysis module F226) may be configured in any suitable manner.

In a seventh variation, as shown in FIG16 , the microbiome characterization module 221 (e.g., analysis module G 227) can process features identified as being statistically associated with one or more microbial-related conditions to compare with other features that are not associated with one or more microbial-related conditions, such as identifying more or less common overall features among those features that are found to be associated or not associated with one or more microbial-related conditions. The microbiome characterization module 221 can generate and/or utilize mappings (e.g., mappings of microbiome features, etc.) to biological annotations such as gene regulatory networks or biochemical pathways. However, the microbiome characterization module 221 (e.g., analysis module G 227) can be configured in any suitable manner.

As shown in FIG. 17 , the microbiome characterization system 220 may preferably perform multi-site analysis of associated samples collected from multiple sites (e.g., performing multi-site analysis with a microbiome characterization module 221 based on a multi-site microbial dataset associated with different collection sites; generating a multi-site characterization based on the output of the microbiome characterization module 221; etc.). Sites (e.g., collection sites, etc.) may include any one or more of the following areas: intestinal tract, skin, nose, mouth, genitals, other suitable physiological sites, other sample collection sites, and/or any other suitable sites. Multi-site analysis may be performed at a population level (e.g., with respect to different populations for identifying microbiome features and/or generating associated models such as different models, customizing the different models to analyze datasets associated with multiple collection sites, etc.), at an individual level (e.g., for a user), and/or for any suitable entity. Multi-site analysis may be performed using and/or based on one or more microbiome characterization modules 221 (e.g., based on their output, etc.) and/or any other suitable component (e.g., a remote computing system, a user device, etc.). For example, system 200 may include: a sample processing network operable to process (e.g., collect, sequence, etc.) biological samples including site-diversity samples collected from multiple collection sites, the collection sites including at least two of the intestine, genitals, mouth, skin, and nose; and a first microbiome characterization module 221 operable to apply a first statistical test (e.g., a univariate statistical test, etc.) (and/or other suitable analytical techniques) to determine a first subset of microbiome features of a microbiome feature set based on the site-diversity samples, wherein each subset of microbiome features from the first subset of microbiome features corresponds to a different collection site from the multiple collection sites (e.g., different or similar types of microbiome features for different collection sites based on different microbiome compositions and/or functions for different collection sites, etc.). In this embodiment, the system 200 may include a second microbiome characterization module 221 that is operable to apply an additional statistical test (e.g., a univariate statistical test; a different type of statistical test compared to the first statistical test, such as a different univariate statistical test; etc.) to determine a second subset of microbiome features of the microbiome feature set based on the site diversity sample (e.g., wherein the first subset of microbiome features corresponds to the first statistical test, and wherein different subsets of the first subset correspond to different collection sites; wherein the second subset of microbiome features corresponds to the additional statistical test, and wherein different subsets of the second subset correspond to different collection sites; etc.), and wherein a microbiome-related condition model is generated based on the first subset and the second subset of microbiome features (e.g., a model for multi-site analysis; wherein multiple microbiome-related condition models can be generated based on the microbiome features, such as different models for different collection sites and/or for different microbiome-related conditions associated with different collection sites, etc.).

Multi-site analysis may include integration, combination, and/or otherwise aggregation of site-wise characterizations (e.g., different site-wise individual propensity scores calculated from different microbial data sets, the different microbial data sets corresponding to samples collected at different collection sites, etc.), site-wise therapeutic intervention promotion, and/or any other suitable process in the case of multi-site analysis. Multi-site analysis (e.g., using microbiome characterization module 221, etc.) may be performed by applying at least one or more of the following: statistical techniques including Bayesian and frequentist methods for processing scores or probabilities, and/or other suitable analysis techniques. In a variation, individual indicators (e.g., propensity scores and/or other indicators for one or more microbial-related conditions) associated with different collection sites (e.g., of a single user, multiple users, etc.) may be combined to determine an overall indicator (e.g., an overall disease propensity score and/or other indicator, etc.), for example, by using an average of the individual indicator values. Standard deviations can be calculated using standard formulas to propagate uncertainty from individual site-related data (e.g., individual propensity scores for individuals, etc.) to overall indicators (e.g., overall disease propensity scores, etc.). In an embodiment, an overall indicator (e.g., multi-site characterization, etc.) can describe additional information relative to any single site-related indicator, and wherein the site-related indicator can provide complementary and non-redundant information. In a specific embodiment, complementarity can indicate that microbiome-related characterizations (e.g., indicators, etc.) corresponding to different sites are not completely associated (e.g., one site of microbiome composition, function, and/or other suitable characterizations cannot perfectly predict the microbiome composition, function, and/or other suitable characterizations of other sites, etc.). Multi-site analysis can account for the redundancy of information between sampling sites (e.g., where failure to do so may result in a biased overall indicator, such as by giving high importance to sites with strong associations between them, etc.). In one variation, the microbiome characterization system 220 can use information about covariance/correlation between sampling (e.g., between microbiome datasets corresponding to samples of different site diversity, etc.), which can be estimated from the corresponding data, such as to determine an improved overall metric (e.g., with increased accuracy, etc.). In one embodiment, multivariate statistical methods (e.g., for estimating covariance and/or correlation, etc.) can be applied to interpret non-redundant information. In a particular embodiment, a particular covariance/correlation pattern between microbiome features (e.g., microbiome composition, microbiome function, microbiome features, microbiome datasets, other suitable aspects of a microbiome profile, etc.) corresponding to the site being considered can be used to estimate the mean and standard deviation. The mean and variance can be obtained by and to estimate, S is the number of sites under consideration, _xi is the site aspect score, and where _σi and _σij are the variance of the i-th site aspect score and the covariance parameter between the i-th site and the j-th site, respectively. These estimates of covariance and/or associations can be performed using multivariate statistical methods. In a particular embodiment, for users with multi-site microbial data, the microbiome characterization system 220 can: apply dimensionality reduction techniques to the data of each site separately, such as by using PCA and selecting a subset of latent variables sufficient to characterize the data; and/or using the latent variables from each site, covariance/association can be estimated using multivariate methods, such as by using canonical association analysis, but any suitable analysis technique and/or microbiome characterization module 221 can be used for multi-site analysis.

In a particular embodiment, as shown in FIG. 17 , an overall propensity score (e.g., for one or more microbial-related conditions) can be determined by one or more of the following: collecting samples from users from two or more collection sites; determining a multi-site microbial dataset (e.g., including site-wise microbial data; by laboratory processing and/or downstream bioinformatics methods; etc.); determining a site-wise propensity score (e.g., based on site-wise microbial group characteristics determined using the microbiome characterization module 221; by a site-wise microbial-related condition propensity estimation algorithm; by an analytical technique comprising at least one of a machine learning model, a regression model, a clustering algorithm that scores a microbial group profile for a disease propensity based on a previously learned parametric or non-parametric function, etc.); and determining an overall propensity score based on the site-wise propensity score, information on non-significant association patterns of site-to-site microbial group profiles, and/or other suitable data. Multi-site analysis (e.g., combining complementary information from different sites to generate an overall index, etc.) can provide a holistic measure of microbial propensity for a condition, which can be integrated with patient phenology to guide diagnostic and treatment decisions (e.g., facilitate therapeutic interventions, etc.). However, the microbiome characterization system and/or other suitable components can be configured in any suitable manner to facilitate multi-site analysis (e.g., applying an analytical technique to a multi-site analysis purpose; generating a multi-site characterization; etc.).

The microbiome characterization system may preferably perform cross-condition analysis for multiple microbiome-related conditions (e.g., using one or more microbiome characterization modules 221; generating multi-conditional characterizations based on the output of the microbiome characterization module 221, such as multi-conditional microbiome features; etc.). For example, the microbiome characterization system may characterize the relationship between microbiome-related conditions based on microbial data, microbiome features, and/or other suitable microbiome characteristics of a user associated with multiple microbiome-related conditions (e.g., diagnosed with multiple microbiome-related conditions, characterized by multiple microbiome-related conditions, etc.). In a particular embodiment, cross-condition analysis may be performed based on characterizations for individual microbiome-related conditions (e.g., outputs from the microbiome characterization module 221 for a single microbiome-related condition, etc.). The cross-condition analysis may include identification of condition-specific features (e.g., associated only with a single microbiome-related condition, etc.), multi-conditional features (e.g., associated with more than two microbiome-related conditions, etc.), and/or any other suitable type of features. Cross-condition analysis can include, for example, determining parameters for reporting association, consistency, and/or other similar parameters describing the relationship between two or more microbial-related conditions by evaluating different pairs of microbial-related conditions, wherein pairs with higher parameter value rankings can be associated with greater similarity (e.g., sharing) of microbial group features. In one embodiment, cross-condition analysis can include joint analysis of data from multiple microbial-related conditions about associated microbial group characteristics (e.g., microbial data, microbial group features, etc.). Cross-condition analysis can include the application of analysis techniques, which include any one or more of the following: multivariate models, canonical association models, multi-label artificial intelligence methods (e.g., multi-label supervised, multi-label unsupervised, multi-label semi-supervised machine learning or artificial intelligence methods, etc.), and/or any other suitable analysis techniques (e.g., for the application of microbial group characterization module 221 in analyzing single microbial-related conditions and comparing the resulting characterizations, etc.). However, the microbial group characterization system and/or other suitable components can be configured in any suitable manner to facilitate cross-condition analysis (e.g., applying analysis techniques to cross-condition analysis purposes; generating cross-condition characterizations, etc.).

The microbiome characterization system 220 preferably includes a remote computing system (e.g., for applying the microbiome characterization module 221, etc.), but may additionally or alternatively include any suitable computing system (e.g., a local computing system, a user device, a processing system component, etc.). However, the microbiome characterization system 220 can be configured in any suitable manner.

The therapy promotion system 230 of the system 200 can function to promote therapeutic interventions (e.g., promote one or more therapies, etc.) for one or more microbial-related conditions (e.g., promote the modulation of the composition and functional diversity of the user's microbiome to improve the user's status with respect to one or more microbial-related conditions, etc.). The therapy promotion system 230 can promote therapeutic interventions for any number of microbial-related conditions, such as based on multi-site characterization, multi-condition characterization, other characterization, and/or any other suitable data, and the any number of microbial-related conditions are associated with any number of collection sites. The therapy promotion system 230 can include any one or more of the following: a communication system (e.g., communicating therapy recommendations, selections, dissuasion, and/or other suitable therapy-related information to a user device and/or a care provider device; enabling telemedicine between a care provider and a subject with respect to a microbial-related condition; etc.), an application executable on a user device (e.g., indicating microbial composition and/or function to a user, etc.), a medical device (e.g., such as a biological sampling device for collecting samples from different collection sites, a drug delivery device, a surgical system, etc.), a user device (e.g., a biometric sensor), and/or any other suitable component. One or more therapy facilitation systems 230 may be controllable, communicable with, and/or otherwise associated with the microbiome characterization system 220. For example, the microbiome characterization system 220 may generate representations of one or more microbiome-related conditions for the therapy facilitation system 230 to present (e.g., transmit, communicate, etc.) to a corresponding user (e.g., at an interface 240, etc.). In another embodiment, the therapy facilitation system 230 may update and/or otherwise modify an application and/or other software of a device (e.g., a user's smartphone) to promote a therapy (e.g., promoting lifestyle changes in a to-do list application to improve a user's status associated with one or more microbiome-related conditions, etc.). However, the therapy facilitation system 230 may be configured in any other manner.

As shown in FIG9 , the system 200 may additionally or alternatively include an interface 240 that may serve to improve the presentation of microbiome features, microbiome-related condition information (e.g., propensity indicators, therapy recommendations, comparisons with other users, other characterizations, etc.). In an embodiment, the interface 240 may present microbiome-related condition information, including: microbiome composition (e.g., taxonomic groups, relative abundance, etc.), functional diversity (e.g., relative abundance of genes associated with a particular function), and propensity indicators for one or more microbiome-related conditions, such as relative to a user group sharing demographic characteristics (e.g., smokers, exercisers, users who adopt different dietary regimens, consumers of probiotics, antibiotic users, groups undergoing specific therapies, etc.). However, the interface 240 may be configured in any suitable manner.

Although the components of system 200 are generally described as distinct components, they may be physically and/or logically integrated in any manner. For example, a computing system (e.g., a remote computing system, a user device, etc.) may implement part and/or all of microbiome characterization system 220 (e.g., applying a microbiome-related condition model to generate a characterization of a microbiome-related condition for a user, etc.) and therapy promotion system 230 (e.g., promoting therapeutic intervention by presenting insights associated with microbiome composition and/or function; presenting therapeutic recommendations and/or information; scheduling daily events in a smartphone calendar application to notify the user to take a probiotic therapy based on characterization identification, etc.). However, the functionality of system 200 may be distributed among any suitable system components in any suitable manner. Additionally or alternatively, system 200 and/or method 100 may include any suitable components and/or functionality similar to those described in U.S. Patent Application No. 14/593,424, filed on January 9, 2015, the entire contents of which are incorporated herein by reference. However, the components of system 200 may be configured in any suitable manner.

4.1 Generating microbial datasets

Block S110 may include determining a microbial dataset associated with a subject set (e.g., a microbial sequence dataset, a microbial composition diversity dataset such as based on a microbial sequence dataset, a microbial functional diversity dataset such as based on a microbial sequence dataset, etc.) S110. Block S110 may function to process biological samples (e.g., an aggregated set of biological samples associated with a subject population, a subpopulation of subjects, a subgroup of subjects sharing demographic characteristics and/or other suitable characteristics) to determine composition, function, pharmacogenomics, and/or other suitable aspects associated with a corresponding microbial group, such as regarding one or more microbial related conditions. The composition and/or functional aspects may include one or more aspects at the microbial level (and/or other suitable granularity), including parameters related to the distribution of microorganisms across different groups of kingdoms, phyla, classes, orders, families, genera, species, subspecies, strains, and/or any other suitable infraspecies taxon (e.g., measured by the total abundance of each group, the relative abundance of each group, the total number of groups represented, etc.). Compositional and/or functional aspects may also be expressed in terms of operational taxonomic units (OTUs). Compositional and/or functional aspects may additionally or alternatively include compositional aspects at the genetic level (e.g., regions determined by multilocus sequence typing, 16S sequences, 18S sequences, ITS sequences, other genetic markers, other phylogenetic markers, etc.). Compositional and functional aspects may include the presence or absence or amount of genes associated with a particular function (e.g., enzymatic activity, transport function, immune activity, etc.). Thus, the output of block S110 can be used to facilitate determination of microbiome features (e.g., generation of microbial sequence data that can be used to identify microbiome features, etc.) for the characterization process of block S130 and/or other suitable portions of method 100 (e.g., where block S110 can result in output of a microbiome composition dataset, a microbiome function dataset, and/or other suitable microbial dataset from which microbiome features can be extracted, etc.), where the features can be microbial-based (e.g., the presence of a bacterial genus), genetic-based (e.g., based on representation of particular genetic regions and/or sequences), functional-based (e.g., the presence of a particular catalytic activity), and/or any other suitable microbiome feature.

In one variation, block S110 can include an evaluation and/or processing based on phylogenetic markers derived from bacteria and/or archaea (e.g., for generating a microbial dataset, etc.) regarding a gene family associated with one or more of the following: ribosomal protein S2, ribosomal protein S3, ribosomal protein S5, ribosomal protein S7, ribosomal protein S8, ribosomal protein S9, ribosomal protein S10, ribosomal protein S11, ribosomal protein S12/S23, ribosomal protein S13, ribosomal protein S15P/S13e, ribosomal protein S17, ribosomal protein S19, ribosomal protein L1, ribosomal protein L2, ribosomal protein L3, ribosomal protein L4/L5, ribosomal protein L6, ribosomal protein L7, ribosomal protein L8, ribosomal protein L9, ribosomal protein S10, ribosomal protein S11, ribosomal protein S12/S23, ribosomal protein S13, ribosomal protein S15P/S13e, ribosomal protein S17, ribosomal protein S19, ribosomal protein L1, ribosomal protein L2, ribosomal protein L3, ribosomal protein L4/L5e, ribosomal protein L6 6. Ribosomal protein L10, ribosomal protein L11, ribosomal protein L14b/L23e, ribosomal protein L15, ribosomal protein L16/L10E, ribosomal protein L18P/L5E, ribosomal protein L22, ribosomal protein L24, ribosomal protein L25/L23, ribosomal protein L29, translation elongation factor EF-2, translation initiation factor IF-2, metalloendopeptidase, ffh signal recognition granule protein, phenylalanyl-tRNA synthetase β subunit, phenylalanyl-tRNA synthetase α subunit, tRNA pseudouridine synthase B, porphobilinogen deaminase, ribosomal protein L13, phosphoribosylformylglycinamidine cyclo-ligase and ribonuclease HII. Additionally or alternatively, markers may include: target sequences (e.g., sequences associated with microbial taxonomic groups; sequences associated with functional aspects; sequences associated with microbial-related conditions; sequences indicating user responsiveness to different therapies; sequences that are invariant across populations and/or any other suitable subject collections, such as to facilitate multiplex amplification using primer types that share primer sequences; conserved sequences; sequences including mutations, polymorphisms; nucleotide sequences; amino acid sequences; etc.), proteins (e.g., serum proteins, antibodies, etc.), peptides, carbohydrates, lipids, other nucleic acids, whole cells, metabolites, natural products, genetic susceptibility biomarkers, diagnostic biomarkers, prognostic biomarkers, predictive biomarkers, other molecular biomarkers, gene expression markers, imaging biomarkers, and/or other suitable markers. However, markers may include any other suitable markers associated with microbiome composition, microbiome function, and/or microbial-related conditions.

Therefore, characterizing the microbiome composition and/or functional aspects of each biological sample aggregation set preferably includes a combination of sample processing techniques (e.g., wet lab techniques; as shown in Figure 5), which sample processing techniques include but are not limited to amplicon sequencing (e.g., 16S, 18S, ITS), unique molecular identifiers (Unique Molecular Identifier, UMI), 3-step polymerase chain reaction (3step PCR), clustered regularly interspaced short palindromic repeat sequences (crispr), metagenomics methods, metatranscriptomics, the use of random primers and computational techniques (e.g., using bioinformatics tools) to quantitatively and/or qualitatively characterize the microbiome and functional aspects associated with each biological sample from a subject or a population of subjects.

In a variation, the sample processing in block S110 may include any one or more of: lysing a biological sample, disrupting membranes in cells of the biological sample, separating undesirable elements (e.g., RNA, proteins) from the biological sample, purifying nucleic acids (e.g., DNA) from the biological sample, amplifying nucleic acids from the biological sample, further purifying the amplified nucleic acids of the biological sample, and sequencing the amplified nucleic acids of the biological sample. In one embodiment, block S110 may include: collecting biological samples from a user set (e.g., biological samples collected by a user using a sampling kit including a sample container, etc.), wherein the biological sample includes a microbial nucleic acid associated with a microbial-related condition (e.g., a microbial nucleic acid including a target sequence associated with a microbial-related condition, etc.). In another embodiment, block S110 may include providing a sampling kit set to the user set, each sampling kit in the sampling kit set including a sample container (e.g., including a pretreatment reagent such as a lysis reagent; etc.), which is operable to receive a biological sample from a user in the user set.

In a variation, the membranes in the cells of the lysing biological sample and/or the destruction of the biological sample preferably include physical methods (e.g., bead beating, nitrogen decompression, homogenization, ultrasonic treatment), which omits certain reagents that produce biases in the sequencing-based representation of certain bacterial groups. Additionally or alternatively, the lysis or destruction in frame S110 can involve chemical methods (e.g., using detergents, using solvents, using surfactants, etc.). Additionally or alternatively, the lysis or destruction in frame S110 can involve biological methods. In a variation, separating undesirable elements can include removing RNA using ribonucleases (RNases) and/or removing proteins using proteases. In variations, purifying nucleic acids may include one or more of the following: precipitation of nucleic acids from a biological sample (e.g., using an alcohol-based precipitation method), liquid-liquid based purification techniques (e.g., phenol-chloroform extraction), chromatography-based purification techniques (e.g., column adsorption), purification techniques involving the use of moiety-bound particles (e.g., magnetic beads, buoyant beads, beads with a size distribution, ultrasound-responsive beads, etc.), which moiety-bound particles are configured to bind nucleic acids and are configured to release nucleic acids in the presence of an elution environment (e.g., having an elution solution, providing a pH change, providing a temperature change, etc.), and any other suitable purification technique.

In a variation, amplification of the purified nucleic acid may include one or more of the following: polymerase chain reaction (PCR)-based techniques (e.g., solid phase PCR, reverse transcription-PCR (RT-PCR), quantitative PCR (qPCR), multiplex PCR, touchdown PCR, nano PCR, nested PCR, hot start PCR, etc.), helicase-dependent amplification (HDA), loop mediated isothermal amplification (LAMP), self-sustained sequence replication (3SR), nucleic acid sequence based amplification (NASBA), strand displacement amplification (SDA), rolling circle amplification (RCA), ligase chain reaction (LCR), and any other suitable amplification technique. In the amplification of the purified nucleic acid, the primers used are preferably selected to prevent or minimize amplification bias, and are configured to amplify a nucleic acid region/sequence (e.g., 16S region, 18S region, ITS region, etc.), which is useful information for diagnosis, preparation (e.g., probiotic preparation) and/or any other suitable purpose, taxonomically and phylogenetically. Therefore, universal primers configured to avoid amplification bias (e.g., F27-R338 primer set for 16S RNA, F515-R806 primer set for 16S RNA, etc.) can be used in amplification. Additionally or alternatively, a combined barcode sequence and/or UMI specific to a biological sample, a user, a microbial-related condition, a taxa, a target sequence, and/or any other suitable component is included, which can facilitate a post-sequencing identification process (e.g., for mapping sequence reads to microbiome composition and/or microbiome functional aspects; etc.). The primers used in the variant of block S110 may additionally or alternatively include an adapter region configured to cooperate with a sequencing technology involving a complementary adapter (e.g., Illumina sequencing). Additionally or alternatively, block S110 may implement any other steps configured to facilitate processing (e.g., using a Nextera kit). In a particular embodiment, performing amplification and/or sample processing operations may be performed in a multiplexed manner (e.g., for a single biological sample, for multiple biological samples across multiple users, etc.). In another particular embodiment, performing amplification may include a standardization step to balance the library and detect all amplicons independent of the amount of starting material, such as 3-step PCR, bead-based standardization, and/or other suitable techniques.

In variations, sequencing of purified nucleic acids can include methods involving targeted amplicon sequencing, metatranscriptome sequencing, and/or metagenomic sequencing, implemented using one or more of the following techniques: synthetic sequencing technology (e.g., Illumina sequencing), capillary sequencing technology (e.g., Sanger sequencing), pyrosequencing technology, and nanopore sequencing technology (e.g., using Oxford Nanopore technology).

In a specific embodiment, amplification and sequencing of nucleic acids from biological samples of a biological sample collection includes solid-phase PCR involving DNA fragments of the biological sample on a substrate using oligonucleotide adapters for bridge amplification, wherein the amplification involves primers having a forward index sequence (e.g., an Illumina forward index corresponding to a NextSeq/HiSeq platform), a forward barcode sequence, a transposase sequence (e.g., a transposase binding site corresponding to a MiSeq/NextSeq/HiSeq platform), an adapter (e.g., a zero, one, or two base fragment configured to reduce uniformity and improve sequence results), additional random bases, a UMI, a sequence for targeting a specific target region (e.g., a 16S region, a 18S region, an ITS region), a reverse index sequence (e.g., an Illumina reverse index corresponding to a MiSeq/HiSeq platform), and a reverse barcode sequence. In a particular embodiment, sequencing may include Illumina sequencing using synthetic sequencing technology (e.g., using a HiSeq platform, using a MiSeq platform, using a NextSeq platform, etc.). In another particular embodiment, method 100 may include: identifying one or more class primer types compatible with one or more genetic targets, the one or more genetic targets being associated with one or more microbial-related conditions (e.g., human behavioral conditions, disease-related conditions, etc.); generating a microbial dataset (e.g., a microbial sequence dataset, etc.) for one or more users (e.g., a subject set) based on one or more primer types (e.g., and microbial nucleic acids included in the collected biological samples, etc.), such as by fragmenting microbial nucleic acids and/or performing multiplex amplification using fragmented microbial nucleic acids based on one or more identified primer types compatible with the genetic targets, the genetic targets being associated with human behavioral conditions; and/or promoting (e.g., providing) a therapy for a user's condition based on a microbial-related characterization derived from the microbial dataset (e.g., a user's microbiome that can selectively adjust at least one of a population size of a desired taxon and a desired microbiome function, etc.).

In a variation, the primers used in block S110, and/or other suitable portions of method 100 (e.g., primer types corresponding to primer sequences; etc.) may include primers associated with protein genes (e.g., for conserved protein gene sequences encoding across multiple taxa, such as to enable multiplex amplification of multiple targets and/or taxa; etc.). Primers may additionally or alternatively be associated with: microbial-associated conditions (e.g., primers compatible with genetic targets including microbial sequence biomarkers for microbes associated with microbial-associated conditions such as human behavioral conditions and/or disease-associated conditions; etc.), microbiome composition features (e.g., primers compatible with identified genetic targets corresponding to microbiome composition features, the microbiome composition features associated with taxonomic groups, the taxonomic groups associated with microbial-associated conditions; genetic sequences from which relative abundance features are derived, etc.), functional diversity features, supplemental features, and/or other suitable functions and/or data. Primers (and/or other suitable molecules, markers, and/or biological materials described herein) may have any suitable size (e.g., sequence length, number of base pairs, conserved sequence length, variable region length, etc.). Additionally or alternatively, any suitable number of primers may be used in sample processing for performing characterization (e.g., microbial-related characterization; etc.), improving sample processing (e.g., by reducing amplification bias, etc.), and/or for any suitable purpose. Primers may be associated with any suitable number of targets, sequences, taxa, conditions, and/or other suitable aspects. Primers used in box S110 (e.g., primer selection based on parameters used in generating a classification database), and/or other suitable parts of method 100 may be selected by the process described in box S110, and/or any other suitable part of method 100. In one embodiment, block S110 may include: identifying a primer type of a microbial nucleic acid sequence associated with a microbial-associated condition (e.g., a primer type for a primer operable to amplify a microbial nucleic acid sequence associated with a microbial-associated condition; etc.); and generating a microbial sequence dataset based on the primer type and the microbial nucleic acid (e.g., using primers of the primer type for amplifying the microbial nucleic acid; and sequencing the amplified nucleic acid to generate a microbial sequence dataset; etc.). In a specific embodiment, block S110 may include: fragmenting microbial nucleic acid fragments; and performing multiple amplification with the fragmented microbial nucleic acid based on the fragmented microbial nucleic acid and the identified primer type associated with the microbial-associated condition. Additionally or alternatively, the primers (and/or processes associated with the primers) may include and/or be similar to the primers (and/or processes associated with the primers) described in U.S. Patent Application No. 14/919,614 filed on October 21, 2015, the entire contents of which are incorporated herein by reference. However, the identification and/or use of primers may be configured in any suitable manner.

Certain variations of sample processing may include further purification of the amplified nucleic acid (e.g., PCR product) prior to sequencing, which functions to remove excess amplification elements (e.g., primers, deoxyribonucleoside triphosphates (dNTPs), enzymes, salts, etc.) In embodiments, any one or more of the following may be used to facilitate additional purification: purification kits, buffers, alcohols, pH indicators, chaotropic salts, nucleic acid binding filters, centrifugation, and/or any other suitable purification technique.

In variations, the computational processing in box S110 can include any one or more of the following: identification of microbiome-derived sequences (e.g., as opposed to subject sequences and contaminants), alignment and mapping of microbiome-derived sequences (e.g., alignment of fragmented sequences using one or more of single-end alignment, ungapped alignment, gapped alignment, pairwise alignment), and generation of features associated with (e.g., derived from) compositional and/or functional aspects of a microbiome associated with a biological sample.

Identification of microbiome-derived sequences can include mapping sequence data from sample processing to a subject reference genome (eg, provided by the Genome Reference Consortium) in order to remove subject genome-derived sequences. Then, after mapping the sequence data to the subject reference genome, the remaining unidentified sequences can be further clustered into operational taxonomic units (OTUs) based on sequence similarity and/or reference-based methods (e.g., using VAMPS, using MG-RAST, using the QIIME database), aligned using an alignment algorithm (e.g., Basic Local Alignment Search Tool, field programmable gate array (FPGA) accelerated alignment tool, BWT index using BWA, BWT index using SOAP, BWT index using Bowtie, etc.) (e.g., using a gene hashing algorithm, using a Needleman-Wunsch algorithm, using a Smith-Waterman algorithm), and mapped to a reference bacterial genome (e.g., provided by the National Center for Biotechnology Information). The mapping of unidentified sequences can additionally or alternatively include mapping to reference archaeal genomes, viral genomes, and/or eukaryotic genomes. Furthermore, mapping of taxa may be performed with respect to an existing database and/or with respect to a custom generated database.

Once a representative group of microorganisms of a microbial group associated with a biological sample has been identified, it is possible to perform the generation of features associated with (e.g., derived from) the composition and functional aspects of the microbial group, which is associated with the biological sample. In a variation, generating features may include generating features based on multilocus sequence typing (MSLT) to identify markers useful for characterization in subsequent frames of method 100. Additionally or alternatively, the generated features may include generating features describing the presence or absence of certain microbial taxonomic groups and/or the ratios between the displayed microbial taxonomic groups. Additionally or alternatively, generating features may include generating features that describe one or more of the following: the number of taxonomic groups represented, the network of taxonomic groups represented, the associations in the representation of different taxonomic groups, the interactions between different taxonomic groups, the products produced by different taxonomic groups, the interactions between products produced by different taxonomic groups, the ratio between dead and live microorganisms (e.g., for different taxonomic groups represented, based on RNA analysis), phylogenetic distances (e.g., based on Kantorovich-Rubinstein distance, Wasserstein distance, etc.), any other suitable taxonomic group-related features, any other suitable genetic or functional aspects.

Additionally or alternatively, generating features may include generating features describing the relative abundance of different microbial groups, such as using sparCC methods, using genome relative abundance and average size (Genome Relative Abundance and Average size, GAAS) methods and/or using genome relative abundance (Genome Relative Abundance using Mixture Model theory, GRAMMy) methods using mixed model theory, which uses sequence similarity data to perform the maximum likelihood estimation of the relative abundance of one or more microbial groups. Additionally or alternatively, generating features may include generating statistical measurements of classification changes such as derived from abundance indicators. Additionally or alternatively, generating features may include generating features associated with (e.g., derived from) relative abundance factors (e.g., changes in taxonomic abundance affecting other taxonomic units). Additionally or alternatively, generating features may include isolating and/or combining to generate qualitative features describing the presence of one or more taxonomic groups. Additionally or alternatively, generating features may include the generation of features related to genetic markers (e.g., representative 16S, 18S and/or ITS sequences) that characterize the microorganisms of the microbial group associated with the biological sample. Additionally or alternatively, generating features may include generating features related to the functional association of specific genes and/or organisms with specific genes. Additionally or alternatively, generating features may include generating features related to the pathogenicity of taxa and/or products attributed to taxa. However, frame S120 may include the generation of any other suitable features of sequencing and mapping of nucleic acids derived from biological samples. For example, features may be combined (e.g., involving pairs, three groups), associated (e.g., related to the association between different features) and/or related to changes in features (e.g., time changes, changes across sample sites, etc., spatial changes, etc.). However, processing biological samples, generating microbial data sets and/or other aspects associated with frame S110 may be performed in any suitable manner.

4.2 Processing of Supplementary Datasets

Method 100 may additionally or alternatively include block S120, which may include processing (e.g., receiving, collecting, converting, etc.) a supplemental data set associated with one or more microbe-related conditions (e.g., human behavior conditions such as associated user behavior; disease-related conditions such as associated medical history, symptoms, medication, etc.) for the user set (e.g., providing information thereof; description thereof; indication thereof; etc.). Block S120 may function to obtain data associated with one or more subjects in the subject set, which may be used to train, validate, apply, and/or otherwise inform a microbe-related characterization process (e.g., in block S130). In block S120, the supplemental data set preferably includes survey-derived data, but may additionally or alternatively include any one or more of the following: site-specific data (e.g., data providing information about different collection sites, etc.), microbial-related condition data (e.g., data providing information about microbial-related conditions, etc.), contextual data derived from sensors (e.g., wearable device data, etc.), medical data (e.g., current and historical medical data; medical device-derived data; data associated with medical tests; etc.), social media data, user data (e.g., associated sensor data, demographic data, etc.), mobile phone data (e.g., mobile phone application data, etc.), web application data, prior biological knowledge (e.g., information providing microbial-related conditions, microbiome characteristics, associations between microbiome characteristics and microbial-related conditions, etc.), and/or any other suitable type of data. In a variation of block S120 that includes receiving survey-derived data, the survey-derived data preferably provides physiological information, demographic information, and behavioral information associated with the subject. The physiological information may include information related to physiological characteristics (e.g., height, weight, body mass index, body fat percentage, body hair level, etc.). Demographic information may include information related to demographic characteristics (e.g., gender, age, race, marital status, number of siblings, socioeconomic status, sexual orientation, etc.). Behavioral information may include information related to one or more of the following: health conditions (e.g., health and disease states), living situations (e.g., living alone, living with pets, living with significant others, living with children, etc.), dietary habits (e.g., alcohol consumption, caffeine consumption, omnivorous, vegetarian, vegan, sugar consumption, acid consumption, consumption of wheat, eggs, soy, nuts, peanuts, shellfish, and/or other suitable food items, etc.), behavioral tendencies (e.g., physical activity levels, drug use, alcohol consumption, habit formation, etc.), different mobility levels (e.g., amount of exercise such as low, moderate, and/or extreme physical exercise activities; related to distance traveled in a given time period; indicated by mobility sensors such as motion and/or location sensors; etc.), different sexual activity levels (e.g., related to number of partners and sexual orientation), and any other suitable behavioral information. Survey-derived data may include quantitative data and/or qualitative data that may be converted to quantitative data (eg, using severity levels, mapping qualitative responses to quantitative scores, etc.).

In facilitating receipt of survey-derived data, block 130 may include providing one or more surveys to a subject in the subject population, or to an entity associated with a subject in the subject population. Surveys may be provided in person (e.g., in coordination with sample provision and receipt from the subject), electronically (e.g., during account setup by the subject, in an application executed on the subject's electronic device, in a web application accessible via an Internet connection, etc.), and/or in any other suitable manner.

Additionally or alternatively, portions of the supplemental data set may be derived from sensors associated with the subject (e.g., sensors of a wearable computing device, sensors of a mobile device, biometric sensors associated with a user, etc.). Thus, block S130 may include receiving one or more of: physical activity or physical movement related data (e.g., accelerometer and gyroscope data from a subject's mobile device or wearable electronic device), environmental data (e.g., temperature data, altitude data, climate data, light parameter data, etc.), patient nutrition or diet related data (e.g., data from a food establishment check-in, data from a spectrophotometric analysis, user input, nutritional data associated with probiotic and/or prebiotic food items, types of food consumed, amounts of food consumed, diet, etc.), biometric data (e.g., data recorded by sensors within a patient's mobile computing device, data recorded by a wearable or other peripheral device in communication with the patient's mobile computing device), location data (e.g., using a GPS element), and any other suitable data. In a variation, the sensor data may include data sampled in one or more of the following: optical sensors (e.g., image sensors, light sensors, etc.), audio sensors, temperature sensors, volatile compound sensors, weight sensors, humidity sensors, depth sensors, location sensors (GPS receivers, etc.), inertial sensors (e.g., accelerometers, gyroscopes, magnetometers, etc.), biometric sensors (e.g., heart rate sensors, fingerprint sensors, bioimpedance sensors, etc.), pressure sensors, flow sensors, power sensors (e.g., Hall effect sensors), and/or any other suitable sensors.

Additionally or alternatively, portions of the supplemental data set may be derived from the subject's medical record data and/or clinical data. Thus, portions of the supplemental data set may be derived from one or more electronic health records (EHRs) of the subject.

Additionally or alternatively, the supplemental data set of block S120 may include any other suitable diagnostic information (e.g., clinical diagnostic information) that may be combined with the analysis derived from the features to support characterization of the subject in subsequent blocks of method 100. For example, information derived from a colonoscopy, a biopsy, a blood test, diagnostic imaging, other suitable diagnostic procedures, information related to investigations, and/or any other suitable examination may be used to supplement (e.g., for use in any suitable portion of method 100).

Additionally or alternatively, the supplemental data set may include therapy-related data, including one or more of the following: a regimen, a therapy type, a recommended therapy, a therapy used by a user, therapy adherence, etc. For example, the supplemental data set may include user adherence to a recommended therapy (e.g., medication adherence, probiotic adherence, physical exercise adherence, dietary adherence, etc.). However, processing the supplemental data set may be performed in any suitable manner.

4.3 Execution Characterization Process

Block S130 may include, for example, applying analysis techniques with one or more microbiome characterization modules to perform characterization processes (e.g., preprocessing, feature generation, feature processing, multi-site characterization for multiple collection sites, cross-condition analysis for multiple microbiome-related conditions, model generation, etc.) S130 based on (e.g., derived in block S110, etc.) a microbiome dataset and/or other suitable data (e.g., a supplemental dataset; etc.). Block S130 may function to identify, determine, extract, and/or otherwise process features and/or feature combinations that may be used to determine microbiome-related characterizations for a user and/or user set based on the microbiome composition (e.g., microbiome composition diversity features, etc.), function (e.g., microbiome functional diversity features, etc.) of the user or user group, and/or other suitable microbiome features (e.g., such as by generating and applying a characterization model for determining microbiome-related characterizations, etc.). In this way, the characterization process can be used as a diagnostic tool that can characterize a subject (e.g., by behavioral trait, by medical condition, by demographic trait, etc.) based on the subject's microbiome composition and/or functional characteristics, with respect to one or more health status states (e.g., microbiome-related status states), behavioral traits, medical conditions, demographic traits, and/or any other suitable traits of the subject. These characterizations can be used to determine, suggest, and/or provide therapy (e.g., personalized therapy such as determined by a therapy model, etc.) and/or otherwise facilitate therapeutic intervention.

Performing the characterization process S130 may include preprocessing the microbial data set, microbial group features, and/or other suitable data to facilitate downstream processing (e.g., determining microbial related features, etc.). In one embodiment, performing the characterization process may include filtering the microbial data set (e.g., such as filtering the microbial sequence data set before applying the analysis technology set to determine the microbial group features, etc.) by at least one of the following: a) removing first sample data corresponding to a first sample outlier of the biological sample set (e.g., associated with one or more microbial related conditions, etc.), such as where the first sample outlier is determined by at least one of principal component analysis, dimensionality reduction technology, and multivariate methodology; b) removing second sample data corresponding to a second sample outlier of the biological sample set, wherein the second sample outlier can be determined based on the corresponding data quality for the microbial group feature set (e.g., removing samples corresponding to several microbial group features with high-quality data below a threshold condition, etc.); and c) removing one or more microbial group features from the microbial group feature set based on the number of samples of the microbial group features that do not meet the threshold sample number condition, wherein the number of samples corresponds to the number of samples associated with high-quality data of the microbial group features. However, pre-processing may be performed in any suitable manner using any suitable analysis technique.

In performing the characterization process, box S130 can use computational methods (e.g., statistical methods, machine learning methods, artificial intelligence methods, bioinformatics methods, etc.) to characterize the subject as exhibiting characteristics associated with one or more microbial-related conditions (e.g., characteristic characteristics of a set of users having one or more microbial-related conditions, etc.).

Box S130 preferably includes applying one or more analysis techniques (e.g., for determining microbiome features, generating microbial-related representations, etc.) with one or more microbiome characterization modules. For example, applying a set of analysis techniques to determine a set of microbiome features may include, for example, determining an initial set of microbiome features (based on a microbial sequence data set, etc.) with a first microbiome characterization module (e.g., analysis module B, etc.) of a set of microbiome characterization modules, applying one or more dimensionality reduction techniques on the initial set of microbiome features to determine a set of microbiome features (e.g., wherein the set of microbiome features includes fewer microbiome features than the initial set of microbiome features, etc.), such as wherein the dimensionality reduction technique may include at least one of a missing value ratio, a principal component analysis, a probabilistic principal component analysis, a matrix decomposition technique, a component mixture model, and a feature embedding technique. In one embodiment, determining an initial set of microbiome features may include determining an initial set of microbiome features with a second microbiome characterization module (e.g., analysis module A, etc.) of a set of microbiome characterization modules. ), applying one or more statistical tests (e.g., univariate statistical tests, multivariate, etc.) to, for example, determine an initial set of microbiome features based on a microbial sequence data set, etc.), for example, wherein the statistical test (e.g., univariate statistical test, multivariate, etc.) may include at least one of a t-test, a Kolmogorov-Smirnov test, and a regression model. In one embodiment, method 100 may include a second microbiome characterization module (e.g., analysis module C, etc.) in a microbiome characterization module set, applying a machine learning method (and/or other suitable artificial intelligence methods, etc.) to determine a correlation score of a microbiome feature set, wherein generating a microbiome-related condition model may include generating a microbiome-related condition model based on the microbiome feature set and the correlation score (e.g., for determining a characterization of one or more microbiome-related conditions, etc.).

The characterization process (and/or other suitable parts of the method 100 and/or system 200) may be performed for any suitable type and/or number of microorganism-related conditions. In a variation, the characterization process may be performed for one or more skin-related conditions. In one example, for a subject associated with one or more skin-related conditions (e.g., skin photosensitivity; dandruff; dry skin; presence; absence; etc.), method 100 may include: determining a microbial dataset (e.g., a microbial sequence dataset generated from sequenced microbial nucleic acids from a biological sample, the microbial sample being the subject, such as collected at different collection sites, etc.); and using a microbiome characterization module in a microbiome characterization module set (e.g., analysis module A, etc.), based on the microbial datasets corresponding to different collection sites of the subject, applying multiple statistical tests (e.g., Kolmogorov-Smirnov, β-binomial regression and zero-inflated β-binomial regression tests, univariate statistical tests, multivariate statistical tests, etc.) to determine a microbiome feature subset, each microbiome feature subset corresponding to a different collection site, a different microbiome-related condition (e.g., different skin-related conditions, etc.), a different applied statistical test (e.g., as shown in Tables 1, 2, 3, 4, and 5, etc.), a different combination of these entities and/or any other suitable entities. In this embodiment, performing the characterization process may include using a second microbiome characterization module (e.g., analysis module B, etc.) of the microbiome characterization module set to apply a dimensionality reduction technique (e.g., supervised and/or unsupervised dimensionality reduction technique, etc.) to obtain a distance matrix calculated from microbiome features (e.g., microbiome features, microbial data sets, etc.), wherein such data can be used with a machine learning method (and/or other suitable artificial intelligence method) to select a subset of features (e.g., the most relevant features for one or more microbial related conditions, etc.). In a specific embodiment, performing the characterization process may include: determining a feature relevance score and/or other suitable indicators associated with feature importance (e.g., by applying a random forest technique); and using the feature relevance score and/or other suitable indicators with supplemental data (e.g., prior biological knowledge that provides information about microbiome features, such as using a third microbiome characterization module, analysis module F, etc.) to obtain (e.g., using any suitable software tool) sample-level quantification of microbiome functional features. In another particular embodiment, microbiome features can be integrated into (e.g., assigned, such as by software assignment, etc.) microbiome subsystems (e.g., aggregations of microbiome features, groups of microbiome features, etc.), such as based on determination of one or more correlation coefficients between microbiome functional features on analyzed samples and abundance profiles of principal components of the subsystems.

In another variation, the characterization process may be performed for one or more gastrointestinal tract-related conditions. In one embodiment, for a subject associated with one or more gastrointestinal tract-related conditions (e.g., inflammatory bowel disease; presence; absence; etc.), method 100 may include: determining a microbial dataset (e.g., corresponding to different collection sites; etc.); and using a microbiome characterization module of a microbiome characterization module set to determine a microbiome feature subset based on the microbial dataset corresponding to the different collection sites of the subject, applying multiple statistical tests (e.g., Kolmogorov-Smirnov, β-binomial regression and zero-inflated β-binomial regression test, etc.), each microbiome feature subset Corresponding to different collection sites, different microbial-related conditions (e.g., different skin-related conditions, etc.), different applied statistical tests (e.g., as shown in Tables 15, 16, 17, 18, and 19, etc.), different combinations of these entities and/or any other suitable entities (e.g., where different individual results can be compared, such as to identify the intersection of microbiome features across different applied statistical tests for a given collection site and microbial-related condition, as shown in FIG. 18 , which shows the union and intersection of 484 and 141 microbiome features, respectively, etc.). In this embodiment, performing the characterization process may include: using a second microbiome characterization module (e.g., analysis module B, etc.) of the microbiome characterization module set, applying a dimensionality reduction technique (e.g., supervised and/or unsupervised dimensionality reduction technique, etc.) to construct a correlation network between microbiome features, which can be used, for example, through a suitable software tool and/or installation package (package), to identify a collection of interrelated features (e.g., microbiome subsystems, etc.). In this embodiment, performing the characterization process may include determining a summary variable for each microbiome subsystem (e.g., each interrelated microbiome feature set, etc.), such as by applying a PCA method to obtain a single number for each sample, the single number being a subject summarizing the microbiome features (e.g., microbiome profiles, etc.) for the microbiome features included in the microbiome subsystem. In this embodiment, software tools and/or other suitable techniques may be used for network construction and microbiome subsystem detection, such as for empirically determining appropriate analysis parameters. In a particular embodiment, for a soft threshold power, a possible value set between 1 and 20 may be selected (e.g., a power value of 2 is selected to describe a network that maintains high connectivity and relatively clear subsystem detection, etc.), such as shown in Figure 19, which describes a representative of the dimensionality reduction obtained from applying a microbiome characterization module (e.g., analysis module B, etc.), on which each microbiome subsystem detected is represented by a color of different grayscales. Applying dimensionality reduction techniques can result in: a low-dimensional representation of the original data (e.g., for one of each microbiome subsystem) as an example of a set of principal components, where the dimensionality reduction can be up to 47.7x times (e.g., about two orders of magnitude; by converting the 430 microbiome features originally considered for analysis into 9 variables; etc.); and a direct mapping between each microbiome feature and the identified microbiome subsystem (e.g., as shown in Table 20, which describes the obtained mapping, on which each microbiome feature is assigned to a microbiome subsystem, and a soft assignment is also obtained based on the correlation between the feature and the subsystem principal component on the analyzed sample; etc.). In this embodiment, performing the characterization process may include using a third microbiome characterization module (e.g., analysis module F) of the microbiome characterization module set, utilizing supplementary data (e.g., prior biological knowledge of microbiome features, etc.) to obtain sample-level quantification of microbiome functional features (e.g., implemented on a suitable software tool) for integration into the microbiome subsystem for obtaining a soft assignment of microbiome functional features to microbiome subsystems by calculating correlation coefficients between the abundance profiles of the principal components of the subsystem and the microbiome functional features on the analyzed samples (e.g., as shown in Table 21). The output of the microbiome characterization module (e.g., the output of the dimensionality reduction technique; the output of the analysis module B) may be used to generate, execute and/or otherwise process one or more machine learning models (e.g., where the output of the analysis module B may be used as input to the analysis module C and/or other suitable microbiome characterization modules, etc.). In a particular embodiment, microbiome subsystem principal components can be used as predictors of inflammatory bowel disease conditions, which have two labels: cases reporting the condition, and controls not reporting the condition, wherein a machine learning classifier (e.g., a random forest classifier) can be generated to determine feature relevance scores and/or other feature importance indicators (e.g., principal component predictors for determining the most important microbiome subsystems, etc.). In this particular embodiment, as shown in Table 22, the feature importance indicators identify the relevance levels of different microbiome subsystems numbered 5, 2, 6, 0, 3, 1, 4, 7, 8, wherein microbiome subsystem 5 is identified as the most relevant, with a feature importance of ~1.5 times greater than the second more predictive subsystem and ~10 times greater than the least predictive subsystem, wherein microbiome features associated with subsystem 5 are represented in Table 23, and microbiome functional features more closely associated with subsystem 5 are shown in Table 24, and wherein a graphical representation of the interaction between taxonomy and function can be seen in Figure 20. The supplemental data can be used by a microbiome characterization module (e.g., analysis module F), where prior biological knowledge of the relationship between microbiome features and small molecule and drug metabolism can be used to identify drugs that may affect metabolism associated with subsystem 5, other microbiome subsystems, and/or other suitable microbiome features, where in this particular embodiment, 6 of the 22 microbiome features of subsystem 5 have an effect on metabolism of a total of 12 molecules and drugs (e.g., as shown in Table 25), where 4 of the 12 molecules have an effect on inflammation (e.g., associated with inflammatory bowel disease, etc.), and where such a process can identify relevant molecules to determine the selection of drug treatments, such as in the case of acarbose, and dietary and lifestyle changes, such as in the case of resveratrol, taurine, and flavonoids, and/or otherwise facilitate therapeutic intervention. In particular embodiments, determining a characterization may include determining a drug metabolism characterization associated with one or more microbiome-associated conditions, such as based on a microbiome-associated condition model, a sample from a user, a known association between a microbiome feature set and drug metabolism, and/or any other suitable data.

In a variation, performing the characterization process may include performing one or more multi-site analyses associated with multiple collection sites (e.g., using a microbiome characterization module; generating a multi-site characterization, etc.). For example, determining a microbiome-related characterization (e.g., for one or more microbiome-related conditions, etc.) may include: collecting a site-diversity sample set corresponding to multiple collection sites from a user, the collection sites including at least two of the intestines, genitals, mouth, skin, and nose; (e.g., using a microbiome-related condition model generated using a microbiome-related condition module, etc.) determining a site-wise disease propensity indicator set based on the site-diversity sample set, wherein each site-wise disease propensity indicator in the site-wise disease propensity indicator set corresponds to a different collection site in the multiple collection sites (e.g., and is associated with one or more microbiome-related conditions, etc.); and determining a site-wise disease propensity indicator set based on the site-diversity sample set, An overall disease propensity index is determined for a user (e.g., where the overall disease propensity index is associated with one or more microbial-related conditions. In this embodiment, method 100 may include determining a microbial dataset associated with multiple collection sites based on a site diversity sample set, where determining the overall disease propensity index may include: determining at least one of a covariance index and an association index based on the microbial dataset, where at least one of the covariance index and the association index is associated with multiple collection sites; and determining an overall disease propensity index for the user based on the site-wise disease propensity index set and at least one of the covariance index and the association index. However, the multi-site analysis may be performed in any suitable manner.

In variations, performing the characterization process may include performing one or more cross-condition analyses for a plurality of microbiome-related conditions (e.g., using a microbiome characterization module, etc.). In one embodiment, method 100 may include analyzing metadata and microbiome characteristics (e.g., microbiome composition, functionality, etc.) for subjects reporting 26 (and/or other suitable number of) different microbiome-related conditions, one or more of the 26 different microbiome-related conditions including rosacea, celiac disease, photosensitivity, wheat allergy, gluten intolerance (e.g., gluten sensitivity, etc.), dairy allergy, bloating, rheumatoid arthritis, inflammatory bowel syndrome (IBS), hemorrhoidal disease, constipation, reflux, multiple sclerosis, osteoarthritis, ulcerative colitis, Crohn's disease, diarrhea, soy allergy, peanut allergy, tree nut allergy, egg allergy, psoriasis, Hashimoto's thyroiditis, Grave's disease, inflammatory bowel disease, and bloody stools. The microbiome characterization module (e.g., analysis module B and analysis module C, etc.) can be applied to build a predictive model that provides information about specific condition features and multi-condition features (e.g., shared across multiple microbial-related conditions, etc.), wherein performing cross-condition analysis can include, for example, determining microbiome-related parameters based on the multi-condition features, which tell the degree to which the shared microbial-related conditions between two conditions are associated. Performing cross-condition analysis can include: applying dimensionality reduction techniques to a distance matrix calculated from microbiome characteristics (e.g., microbiome features, microbial datasets, etc.); and using latent variables with machine learning models and/or other suitable artificial intelligence methods. In a specific embodiment, performing cross-condition analysis can include determining Bray-Curtis differences between microbiome features (e.g., for different samples corresponding to different subjects, etc.); applying the resulting sample dissimilarity matrix as input to a singular value decomposition to derive principal components and eigenvalues; and performing additional analysis on principal components that explain more than 1/1000 of the total variance of the data. Subsequent cross-conditional analysis can be performed, for example, including applying a machine learning model and/or other suitable artificial intelligence method, such as Bayesian multi-kernel regression, with a microbiome characterization module (e.g., analysis module C) to obtain quantification of cross-conditional associations explained by microbiome characteristics. Performing the cross-conditional analysis can include quantifying the associations between conditions explained by microbiome characteristics and the covariance between microbial-related conditions explained by microbiome characteristics using a multivariate variance component model that estimates the variance of each microbial-related condition (e.g., phenotype) associated with the microbiome. In a specific embodiment, performing the cross-conditional analysis can include fitting a binary variance component model of the following form: y=u+u ₀ +u ₁ +∈, where u ₁ ～N(0,G ₁ ), in and and where u ₀ captures the quantifies the joint effect on the two phenotypes, while _u1 captures the and Quantitative phenotype-specific effects. In certain embodiments, the covariance of the phenotype can be constructed as lead to The microbiome-mediated correlation estimates for each trait are and In certain embodiments, co-correlation can be calculated as Similar to co-inheritance in quantitative genetics terms. For any trait, x can correspond to a subset of principal components obtained from the singular value decomposition of the sample Bray-Curtis similarity matrix. Suitable software tools can be used to fit the model. Gender, age and/or other suitable user data can be included in the analysis as fixed-effect covariates. In another embodiment, method 100 may include determining multi-conditional microbiome features, wherein determining multi-conditional microbiome features includes using a first microbiome characterization module (e.g., analysis module B, etc.) of a microbiome characterization module set, applying a dimensionality reduction technique to an initial set of microbiome features determined based on a microbial sequence data set; using a second microbiome characterization module of a microbiome characterization module set, determining a cross-conditional association index between different conditions of a plurality of microbial-related conditions; and based on the cross-conditional association index, a multi-conditional microbiome feature set and a sample from a user, determining a multi-conditional characterization. In this embodiment, determining a multi-conditional characterization for a user may include determining a characterization of additional user conditions in a plurality of microorganism-related conditions based on a current user condition in a plurality of microorganism-related conditions (e.g., based on comorbidity between microorganism-related conditions, based on correlation between microorganism-related conditions; etc.), a multi-conditional microbiome feature set, a sample from a user, and a cross-conditional correlation index. In this embodiment, determining a cross-conditional correlation index with a second microbiome characterization module may include applying at least one of a multivariate model, a typical correlation model, and a multi-label artificial intelligence method for different conditions in a plurality of microorganism-related conditions. However, it may be performed in any suitable manner to determine a cross-conditional correlation index, other suitable indicators associated with a cross-conditional analysis, and/or perform other suitable cross-conditional analyses.

Performing cross-condition analysis can include identifying groups (e.g., clusters) of microbial-related conditions, such as groups of microbial-related conditions that have similar patterns of shared microbial group characteristics (e.g., shared microbial group associations, etc.). For example, method 100 can include: determining a set of microbial-related condition groups from multiple microbial-related conditions based on multi-conditional microbial group features (e.g., determined using a microbial group characterization module, etc.); and facilitating therapeutic intervention for microbial-related conditions based on the set of microbial-related condition groups (e.g., and multi-condition characterizations, etc.). In one embodiment, identifying the group can include: performing unsupervised hierarchical clustering, where the input can include a proportional association matrix A distance matrix is calculated by Spearman correlation between rows to estimate the distance of the rows; and the distance matrix is used as input for hierarchical clustering. In this embodiment, Bayesian multiple kernel regression can be used to identify a substantial but variable fraction of phenotypic variance explained by microbial data (e.g., microbiome signatures), wherein in a particular embodiment, the explained variance (R ² ) ranges from 63% for ulcerative colitis to 10% for photosensitivity (e.g., as shown in FIG. 21 and Table 26). In this embodiment, application of multivariate mixed models can be used to estimate microbiome-related associations co- _r12 between 325 pairs of diseases, where the results can be used to use cluster analysis based on microbiome associations to obtain a data-driven arrangement of the microbiome-related conditions being analyzed (e.g., as shown in Figures 22 and 25), and where the hierarchical organization can result in six groups of microbiome-related conditions (e.g., clusters, as shown in Table 27; as shown in Figure 25, where numbers along the diagonal indicate individuals with comorbidity within a given group, e.g., where they report the same group of microbiome-related conditions, and where numbers off-diagonal indicate individuals with comorbidity across groups, such as reporting at least one condition for each group corresponding to the off-diagonal points; etc.). Statistically significant pairs of conditions can be identified. In this embodiment, multiple testing correction can result in identifying 75 of 325 (23%) associations as significantly associated (Bonferroni-corrected p-value < 0.05), which can include 52 of 75 between-group associations (69%) in 10 of 15 pairs where Cluster V and Cluster VI have a more intercluster significant correlation than expected (binomial p-value = 2× ^10-10 ; observed = 76%, 23 of 30; expected = 24%, 79 of 325), and where these clusters are characterized by autoimmune and allergic conditions (e.g., where a summary of the correlations can be shown in Table 27, etc.). In embodiments, cross-conditional analyses can indicate disease comorbidity, such as with respect to the human gut microbiome and/or other suitable microbiomes corresponding to other sites, etc.). In embodiments, the derived data supports associations between the human gut microbiome and multiple conditions (e.g., comorbid conditions, etc.), such as where the derived data may show a microbiome that explains significant variation in variance in the context of multiple autoimmune diseases (e.g., R ² =0.69 for ulcerative colitis; R ² =0.49 for Hashimoto's thyroiditis; R ² =0.69 for Crohn's disease; etc.).

In this example, the cross-condition analysis can lead to the identification of six microbiome-related condition groups: Cluster I (e.g., as shown in Table 28, for co-occurrence, etc.), which includes wheat and gluten-related disorders, as well as rosacea and skin photosensitivity; Cluster II, which includes dairy allergy (e.g., as shown in Table 29, etc.), rheumatoid arthritis (RA), and bloating; Cluster III, which includes irritable bowel syndrome (IBS) (e.g., as shown in Table 30, for co-occurrence with inflammatory bowel disease (IBD) and other microbiome-related conditions, etc.), reflux, constipation, and hemorrhoids; Cluster IV, which includes multiple sclerosis (MS), and inflammatory bowel disease (IBD). Cluster V includes ulcerative colitis and Crohn's disease, two subtypes of IBD, and symptomatic diarrhea that is common in both conditions; Cluster VI includes remaining food allergies (e.g., soy allergy, peanut allergy, tree nut allergy, and egg allergy) and autoimmune diseases (e.g., psoriasis, Hashimoto's thyroiditis, Grave's disease, and IBD). In one embodiment, the set of microbial-related condition groups may include at least one of the following: a first group including allergy-related conditions; a second group including locomotive-related conditions; and a third group including gastrointestinal-related conditions, and wherein promoting therapeutic intervention may include promoting therapeutic intervention for microbial-related conditions based on multi-condition characterization and at least one of the first, second, and third groups (e.g., based on classifying microbial-related conditions into clusters, etc.). In one embodiment, scores for women and men with different numbers of comorbidities may be calculated (e.g., as shown in Table 31).

Performing cross-condition analysis can be used in facilitating therapeutic intervention. Performing cross-condition analysis can be used to group microbial-related conditions to identify biologically related groups of conditions, which can facilitate therapeutic interventions by stratifying users on their microbial group characteristics and the risk of comorbid conditions, such as for multi-level therapeutic interventions including primary defense, early screening, development of personalized therapies, and/or any other suitable therapeutic applications. Microbiome-driven classification (e.g., clustering, etc.) of microbial-related conditions can stratify users for facilitating prevention, diagnosis, treatment, and/or other suitable therapeutic intervention-related processes, such as for prioritizing therapies and/or improving conditions in the same group and/or discouraging therapies that show opposite results among the group. For example, promoting a therapeutic intervention may include at least one of the following: a) promoting a first therapy for a user based on assigning the user to at least one microbe-related condition group in a set of microbe-related condition groups (e.g., identified by one or more microbe group characterization modules using analysis techniques described herein; etc.); b) promoting a second therapy for a user based on associations between microbe-related conditions belonging to the same microbe-related condition group in the set of microbe-related condition groups; and c) discouraging a third therapy for a user based on associations between microbe-related conditions belonging to different microbe-related condition groups in the set of microbe-related condition groups. However, cross-condition analysis and/or any other suitable characterization process may be used in any suitable manner to promote therapeutic intervention.

In one variation, the characterization can be based on features associated with (e.g., derived from) statistical analysis (e.g., analysis of probability distribution) of similarities and/or differences between a first group of subjects that exhibit a target state (e.g., a microbial-related condition) and a second group of subjects that do not exhibit the target state (e.g., a "normal" state). In implementing this variation, one or more of a Kolmogorov-Smirnov (KS) test, a permutation test, a Cramer-von Mises test, any other statistical test (e.g., a t-test, a z-test, a chi-squared test, a test associated with a distribution), and/or other suitable analytical techniques can be used. In particular, one or more such statistical hypothesis tests can be used to evaluate a set of features that have different abundances in a first group of subjects that exhibit a target state (e.g., a diseased state) and a second group of subjects that do not exhibit the target state (e.g., having a normal state). . In more detail, the evaluated feature set can be constrained based on the percentage abundance associated with the first group of subjects and the second group of subjects and/or any other suitable parameter belonging to diversity, so as to increase or decrease the characterization confidence (confidence). In a specific implementation of this embodiment, the feature can be derived from a taxon of bacteria, which is abundant in a certain percentage of the first group of subjects and the second group of subjects, wherein the relative abundance of the classification between the first group of subjects and the second group of subjects can be determined from the KS test, with a significant indication (e.g., expressed as a p-value). Therefore, the output of box S130 can include standardized relative abundance values (e.g., the demeanor of the taxon is 25% greater in subjects with microbial-related conditions compared to subjects without microbial-related conditions; sick subjects compared to healthy subjects), with a significant indication (e.g., p-value is 0.0013). The changes in feature generation can be implemented or derived from functional features or metadata features (e.g., non-bacterial markers) in addition or alternatively. Additionally or alternatively, any suitable microbiome signature can be derived based on statistical analysis (e.g., applied to a microbial sequence dataset and/or other suitable microbial dataset, etc.), the statistical analysis comprising any one or more of the following: predictive analysis, multiple hypothesis testing, random forest testing, principal component analysis, and/or other suitable analytical techniques.

In performing the characterization process, block S130 may additionally or alternatively convert input data from at least one of the microbiome compositional diversity dataset and the microbiome functional diversity dataset into a feature vector that may be tested for efficacy in predicting a subject population characterization. Data from the supplemental dataset may be used to provide an indication of one or more characterizations of a characterization set, wherein the characterization process is trained using a training dataset of candidate features and candidate classifications to identify features and/or feature combinations that have a high (or low) predictive power in accurately predicting a classification. In this way, refinement of the characterization process using the training dataset identifies a set of features (e.g., subject features, feature combinations) that are highly correlated with a particular classification of a subject.

In variations, feature vectors (and/or any suitable set of features) effective in the classification of the predictive characterization process may include features related to one or more of: microbiome diversity indicators (e.g., regarding distribution across taxonomic groups, regarding distribution across archaea, bacteria, viruses and/or eukaryotic groups), the presence of taxonomic groups in the microbiome of an individual, representation of specific genetic sequences (e.g., 16S sequences) in the microbiome of an individual, the relative abundance of taxonomic groups in the microbiome of an individual, microbiome resilience indicators (e.g., in response to perturbations determined from supplementary datasets), the abundance of genes encoding proteins or RNAs with a given function (e.g., enzymes, transporters, proteins from the immune system, hormones, interfering RNAs, etc.), and any other suitable features associated with (e.g., derived from) a microbiome diversity dataset and/or a supplementary dataset. In a variation, a microbiome feature may be associated with (e.g., include, correspond to, represent, etc.) at least one of the following: the presence of a microbiome feature from a microbiome feature (e.g., a user microbiome feature, etc.), the absence of a microbiome feature from a microbiome feature, the relative abundance of different taxonomic groups associated with a microbial-related condition; the ratio between at least two microbiome features associated with different taxonomic groups, the interaction between different taxonomic groups, and the phylogenetic distance between different taxonomic groups. In a particular embodiment, a microbiome feature may include one or more relative abundance characteristics associated with at least one of a microbiome composition diversity feature (e.g., relative abundance associated with different taxonomic groups, etc.) and a microbiome functional diversity feature (e.g., relative abundance of sequences corresponding to different functional features; etc.). Relative abundance characteristics and/or other suitable microbiome features (and/or other suitable data described herein) may be extracted and/or otherwise determined based on: standardization, feature vectors derived from at least one of linear latent variable analysis and nonlinear latent variable analysis, linear regression, nonlinear regression, kernel methods, feature embedding methods, machine learning methods, statistical inference methods, and/or other suitable analysis techniques. Additionally or alternatively, a combination of features can be used in a feature vector, where the features can be grouped and/or weighted in providing the combined features as part of a feature set. For example, a feature or feature set can include a weighted synthesis of the number of bacterial species represented in an individual's microbiome, the presence of a particular genus of bacteria in an individual's microbiome, the representation of a particular 16S sequence in an individual's microbiome, and the relative abundance of a first phylum of bacteria to a second phylum of bacteria. However, a feature vector can be additionally or alternatively determined in any other suitable manner.

As shown in FIG. 3 , in one such alternative variation of box S130, a characterization process can be generated and trained according to a random forest predictor (RFP) algorithm, which combines bagging (e.g., bootstrap aggregation) with the selection of a random feature set from a training data set to construct a decision tree set T associated with the random feature set. In using the random forest algorithm, N cases from a decision tree set are randomly sampled with replacement to create a subset of decision trees, and for each node, m prediction features are selected from all prediction features for evaluation. The prediction features that provide the best segmentation at the node (e.g., according to the objective function) are used to perform segmentation (e.g., segmentation at the node into bifurcation and segmentation at the node into trifurcation). By sampling multiple times from a large data set, the strength of the characterization process can be greatly improved in terms of identifying strong features in the prediction classification. In this variation, measures to prevent bias (e.g., sampling bias) and/or address the amount of bias can be included during processing, such as to increase the robustness of the model.

In one variation, block S130 and/or other portions of method 100 may include applying computer-implemented rules (e.g., models, feature selection rules, etc.) to process population-level data, but may additionally or alternatively include applying computer-implemented rules to process microbiome-related data on a specific demographic basis (e.g., a subgroup that shares demographic characteristics such as treatment regimens, dietary regimens, physical activity regimens, race, age, gender, weight, sleep behavior), on a specific condition basis (e.g., a subgroup that exhibits a specific microbiome-related condition, a combination of microbiome-related conditions, triggers of microbiome-related conditions, associated symptoms, etc.), on a sample-specific type basis (e.g., applying different computer-implemented rules to process microbiome data derived from different collection sites; etc.), on a user basis (e.g., different computer-implemented rules for different users), and/or any other suitable basis. Thus, block S130 may include assigning users from a user population to one or more subgroups; and applying different computer-implemented rules to determine features for different subgroups (e.g., a set of feature types used; a type of characterization model generated from the features; etc.). However, applying computer-implemented rules may be performed in any suitable manner.

In another variation, block S130 may include processing (e.g., generating, training, updating, executing, storing, etc.) one or more characterization models (e.g., microbe-related condition characterization models, etc.) for one or more microbe-related conditions (e.g., for outputting a characterization of a user's microbiome characteristics regarding a microbe-related condition to a user, etc.). The characterization model preferably utilizes microbiome features as input, and preferably outputs microbe-related characterizations and/or any suitable components thereof; however, the characterization model and suitable inputs may be used to generate any suitable output. In one embodiment, block S130 may include converting supplemental data, microbiome compositional diversity features and microbiome functional diversity features, other microbiome features, outputs of a microbiome characterization module, and/or other suitable data into one or more characterization models for one or more microbe-related conditions (e.g., training a microbe-related characterization model based on supplemental data and microbiome features; etc.). In another embodiment, method 100 may include: determining a population microbial sequence dataset for a user population associated with one or more microbial-related conditions (e.g., including microbial sequence outputs for different users of the population; etc.) based on a set of samples from a user population; collecting a supplementary dataset associated with the diagnosis of one or more microbial-related conditions for a subject population; and generating a microbial-related condition characterization model based on the population microbial sequence dataset and the supplementary dataset.

In another variation, as shown in FIGS. 8A-8C , different microbiome-related characterization models and/or other suitable models (e.g., generated using different algorithms, using different feature sets, using different input and/or output types, applied in different ways such as with respect to time, frequency, components of an application model, etc.) may be generated for different microbiome-related conditions, different user demographics (e.g., based on age, gender, weight, height, race, etc.), different physiological sites (e.g., gut site model, nasal site model, skin site model, oral site model, genital site model, etc.), individual users, supplemental data (e.g., models that incorporate prior knowledge of microbiome characteristics, microbiome-related conditions, and/or other suitable components; features associated with biometric sensor data and/or survey response data versus models independent of supplemental data, etc.), and/or other suitable criteria.

In variations, determining microbe-related characterizations and/or any other suitable characterizations can include determining microbe-related characterizations about a particular physiological site (e.g., the intestine, healthy intestine, skin, nose, mouth, genitals, other suitable physiological sites, other sample collection sites, etc.) such as by any one or more of the following: determining microbe-related characterizations based on a characterization model derived from site-specific data (e.g., defining associations between microbe-related conditions and microbiome characteristics associated with one or more physiological sites); determining microbe-related characterizations based on a user biological sample collected at one or more physiological sites, and/or any other suitable site-related process. In an embodiment, a machine learning method (e.g., a classifier, a deep learning algorithm), a parameter optimization method (e.g., a Bayesian parameter optimization), a validation method (e.g., a cross-validation method), a statistical test (e.g., a univariate statistical technique, a multivariate statistical technique, an association analysis such as a canonical association analysis, etc.), a dimensionality reduction technique, and/or other suitable analysis techniques (e.g., as described herein) may be applied to determine site-related (e.g., physiological site-related, etc.) characterizations (e.g., for one or more sample collection sites, such as for each type of sample collection site, using one or more methods, etc.), other suitable characterizations, therapies, and/or any other suitable outputs. In a particular embodiment, performing a characterization process (e.g., determining microbial-related characterizations; determining microbial group characteristics; based on a microbial-related characterization model; etc.) may include applying at least one of the following: a machine learning method, a parameter optimization method, a statistical test, a dimensionality reduction method, and/or other suitable methods (e.g., wherein microbial group characteristics such as a microbial group composition diversity feature set and/or a microbial group functional diversity feature set can be associated with microorganisms collected at at least one of an intestinal site, a skin site, a nasal site, an oral site, a genital site, etc.). In another particular embodiment, characterization processes performed for multiple sample collection sites may be used to generate a single characterization that may be combined to determine an aggregate characterization (e.g., an aggregate microbial component value, such as for one or more conditions described herein, etc.). However, method 100 may include determining any suitable site-related (e.g., site-specific) output, and/or perform any suitable portion of method 100 (e.g., collecting samples, processing samples, determining therapy) in any suitable manner, with site-specificity and other site-relatedness.

Characterization of the subject may additionally or alternatively implement the use of high false positive tests and/or high false negative tests to further analyze the sensitivity of the characterization process in supporting analyses generated according to embodiments of method 100. However, performing the characterization process S130 may be performed in any suitable manner.

4.3.1 Skin-related characterization processes

Executing the characterization process S130 may include executing a skin-related characterization process (e.g., determining characterizations for one or more skin-related conditions; determining and/or applying one or more skin-related characterization models, such as models that apply one or more analysis techniques associated with one or more microbiome characterization modules; applying one or more analysis techniques with one or more microbiome characterization modules to generate skin-related characterizations for one or more skin-related conditions, such as comorbid skin-related conditions; determining skin-related characterizations for use in determining and/or promoting one or more therapies for one or more skin-related conditions; etc.) S135, such as for one or more users (e.g., for data corresponding to samples from a collection of subjects, for generating skin-related characterizations for the users; for a single user, for generating skin-related characterizations for the user, such as by using one or more skin-related characterization models; etc.) and/or for one or more skin-related conditions (e.g., using any suitable type and number of microbiome characterization modules, cross-condition analysis, etc.).

In one variation, performing the skin-related characterization process may include determining microbiome features associated with one or more skin-related conditions. In one embodiment, performing the skin-related characterization process may include applying one or more analytical techniques (e.g., statistical analysis) to identify a set of microbiome features (e.g., microbiome composition features, microbiome composition diversity features, microbiome functional features, microbiome functional diversity features, etc.) with the highest correlation with one or more skin-related conditions (e.g., features associated with a single skin-related condition, cross-condition features associated with multiple skin-related conditions and/or other suitable skin-related conditions, etc.). In a specific embodiment, performing the skin-related characterization process may facilitate therapeutic intervention for one or more skin-related conditions, such as by facilitating intervention associated with a therapy that has a positive effect on the status of one or more users with respect to the one or more skin-related conditions. In another specific embodiment, performing a skin-related characterization process (e.g., determining features with the highest association with one or more skin-related conditions, etc.) can be based on a random forest predictor algorithm trained with a training dataset derived from a subset of a subject population (e.g., subjects with one or more skin-related conditions; subjects without one or more skin-related conditions; etc.) and validated with a validation dataset derived from a subset of the subject population. However, determining microbiome features and/or other suitable aspects associated with one or more skin-related conditions can be performed in any suitable manner.

In a variation, performing the skin-related characterization process S135 may include performing a photosensitivity-related condition characterization process for one or more photosensitivity-related conditions. In one embodiment, the skin-related characterization process may identify a feature set with the highest correlation with a photosensitivity-associated condition based on a statistical analysis, and one or more therapies have a positive effect on the photosensitivity-associated condition based on a random forest predictor algorithm trained with a training data set derived from a subset of a subject population and validated with a validation data set derived from a subset of a subject population. In an embodiment, the photosensitivity-associated condition features may include a skin condition characterized by an abnormal response of the skin to electromagnetic spectrum components of sunlight. In an embodiment, the photosensitivity-associated condition may be diagnosed by skin examination, light testing, and light spot testing and/or other suitable methods. The photosensitivity-associated condition may be associated with a specific microbiome diversity and/or a health condition related to the relative abundance of intestinal microorganisms, microorganisms associated with any suitable physiological site, microbiome functional diversity, and/or other suitable microbiome-related aspects.

Microbiome signatures associated with (e.g., positively correlated; negatively correlated; useful for diagnosis; etc.) one or more photosensitivity-associated conditions (and/or other suitable skin-related conditions) may include signatures associated with (e.g., signatures describing their abundance; signatures describing their relative abundance; signatures describing functional aspects associated with them; signatures describing derivations from them; signatures describing their presence and/or absence; etc.) any combination of one or more of the following taxa: Alloprevotella (genus), Prevotella WAL 2039G species (species), Corynebacterium mastitidis (species), Bacteroidaceae (family), Blautia (genus), Bacteroides (genus), Desulfovibrio (genus), Clostridium (genus), Bacteroides vulgatus (species), Faecalibacterium prausnitzii (species), Blautia faecis (species), Alistipes putredinis (species), Bacteroides AR20 (species), Bacteroides AR29 (species), Bacteroides acidifaciens (species), Dielma (genus), Slackia (genus), Eggerthella (genus), Adlercreutzia (genus), Paraprevotella (genus), Alistipes (genus), Holdemania (genus), Eisenbergiella (genus), Enterorhabdus (genus), Adlercreutzia equolifaciens (species), Phascolarctobacterium succinatutens (species), Roseburia inulinivorans (species), Phascolarctobacterium 377 species (species), Desulfovibrio piger (species), Eggertella HGA 1 species (species), Lactonifactor longoviformis (species), HGB 5 species (species), Holdemania filiformis (species), Collinsella intestinalis (species), Neisseria macacae (species), Clostridiaceae (family), Gemella sanguinis (species), Bacteroides fragilis (species), Enterobacteriaeae (family), Lachnospiraceae (family), Pasteurellaceae (family), Pasteurelales (order), Enterobacteriales (order), Sphingobacteriales (order), Haemophilus (genus), Leuconostoc (genus), Brevundimonas (genus), Prevotella oris (species), Odoribacter (genus), Capnocytophaga (genus), Flavobacterium (genus), Pseudomonas brenneri (species), Flavobacterium ceti)(species), Brevundimonas FXJ8.080 species(species), Ruminococcaceae(family), Vibrionaceae(family), Flavobacteriaceae(family), Fusobacteriaceae(family), Porphyromonadaceae(family), Brevibacteriaceae(family), Rhodobacteraceae(family), Intrasporangiaceae(family), Bifidobacteriaceae (Bifidobacteriaceae)(family), Sphingobacteriaceae(family), Caulobacteraceae(family), Campylobacteraceae(family), Bacteroidia(class), Fusobacteriia(class), Flavobacteriia(class), Bifidobacteriales(order), Neisseriales(order), Bacteroida les (order), Rhodobacterales (order), Flavobacteriales (order), Vibrionales (order), Fusobacteriales (order), Caulobacterales (order), Fusobacteria (phylum), Actinobaculum (genus), Varibaculum (genus), Fusicatenibacter (genus), Brevibacterium (genus), m)(genus), Faecalibacterium(genus), Campylobacter(genus), Actinobacillus(genus), Porphyromonas(genus), Fusobacterium(genus), Chryseobacterium(genus), Megasphaera(genus), Rothia(genus), Neisseria(genus), Lactobacillus BL302(species), Bacteroides plebius(genus), plebeius (species), Corynebacterium ulcerans (species), Varibaculum cambriense (species), Blautia wexlerae (species), Staphylococcus WB18-16 species (species), Streptococcus oral taxon G63 species (species), Propionibacterium acnes (species), Anaerococcus 9401487 species (species), Haemophilus parainfluenzae (species), Staphylococcus epidermidis (species), Campylobacter ureolyticus (species), Janibacter M3-5 species (species), Prevotella timoniensis (species), timonensis (species), Peptoniphilus DNF00840 (species), Finegoldia S8 F7 (species), Prevotella disiens (species), Porphyromonas catoniae (species), Fusobacterium periodonticum (species), and/or other suitable taxa (e.g., as described herein); and/or may include functional features (e.g., features describing its abundance; features describing its relative abundance; features describing functional aspects associated with it; features describing its derivation from it; features describing its presence and/or absence; etc.) associated with any combination of one or more of the following: infectious diseases (KEGG2), poorly characterized (Poorly Characterized) (KEGG2), metabolic diseases (KEGG2), immune system diseases (KEGG2), cellular processes and signaling (KEGG2), restriction endonucleases (KEGG3), nucleotide excision repair (KEGG3), and/or other suitable functional characteristics (e.g., as described herein). In a variation, the characterization of the user can include characterizing the user as a person with one or more photosensitive skin-related conditions based on monitoring one or more of the above characteristics in an additional or alternative manner to typical methods of diagnosis and/or treatment.

In a variation, performing the skin-related characterization process S135 may include performing a dry skin-related condition characterization process for one or more dry skin-related conditions. In one embodiment, the skin-related characterization process may be based on statistical analysis to identify a feature set with the highest correlation with a dry skin-related condition, based on a random forest predictor algorithm trained with a training data set derived from a subject population subset and validated with a validation data set derived from a subject population subset, and one or more therapies have a positive effect on the dry skin-related condition. In an embodiment, the dry skin-related condition may include one or more of the following: rough skin, itching, flaking, scaling or peeling, fine lines or cracks, gray skin in dark skin individuals, redness, deep cracks that can bleed and can lead to infection, and/or other suitable dry skin-related conditions. Dry skin-related conditions may be associated with a specific microbiome diversity and/or a health condition related to the relative abundance of intestinal microorganisms, microorganisms associated with any suitable physiological site, microbiome functional diversity, and/or other suitable microbiome-related aspects.

Microbiome signatures associated with (e.g., positively correlated; negatively correlated; useful for diagnosis; etc.) one or more dry skin-related conditions (and/or other suitable skin-related conditions) can include signatures associated with (e.g., signatures describing their abundance; signatures describing their relative abundance; signatures describing functional aspects associated with them; signatures describing being derived from them; signatures describing their presence and/or absence; etc.) any combination of one or more of the following taxa: Corynebacteriaceae (family), Bacilli (class), Lactobacillales (order), Actinomycetes (order), Firmicutes (phylum), Corynebacterium (genus), Dermabacteraceae (family), Lactobacillaceae (family), (family), Propionibacteriaceae (family), Actinobacteria (class), Dermabacter (genus), Dialister (genus), Facklamia (genus), Lactobacillus (genus), Propionibacterium (genus), Corynebacterium (genus), Facklamia hominis (species), Corynebacterium species (species), Propionibacterium MSP09A species (species), Facklamia 1440-97 species (species), Staphylococcus C9I2 species (species), Anaerobic coccus 9402080 species (species), Corynebacterium glucuronolyticum (species), Dermabacterium hominis (species), hominis (species), Enterobacteriaceae (family), Pseudomonadaceae (family), Staphylococcaceae (family), Gammaproteobacteria (class), Bacillusales (order), Enterobacteriales (order), Bifidobacterium (genus), Pseudomonas (genus), Anaerococcus (genus), Kluyvera (genus), Atopobium (genus), Staphylococcus (genus), Lactobacillus BL302 (species), Corynebacterium mastitis (species), Bifidobacterium longum (species), Anaeroglobus geminatus (species), Anaerococcus S9 PR-16 species (species), Prevotella timoniensis (species), Kluyveromyces georginae (species), Actinomycetes (genus), Finegoldia (genus), Cronobacter (genus), Acinetobacter WB22-23 species (species), Anaerococcus octavius (species), Finegoldia S9 AA1-5 species (species), Staphylococcus C-D-MA2 species (species), Peptoniphilus 7-2 species (species), Cronobacter sakazakii (Cronobacter sakazakii (species), Pasteurellaceae (family), Acidobacteriia (class), Sphingobacteriia (class), Sphingobacteriales (order), Acidobacteria (phylum), Porphyromonas (genus), Haemophilus (genus), Acinetobacter (genus), Anaerobic cocci 8405254 species (species), Sphingomonadaceae (family) , Sphingomonadales (order), Kocuria (genus), Geminis (genus), Veillonella CM60 species (species), Lactobacillus 7_1_47FAA (species), Geminis 933-88 species (species), Porphyromonas cardiformis (species), Haemophilus parainfluenzae (species), Bacteroides AR20 species (species), Bacteroides vulgaris (species), Bacteroides D22 species (species), Dorea Longicatena (species), Parabacteroides faecalis (species), merdae (species), Bacteroides AR29 species (species), Dorea (genus), Collinsella (genus), Bacteroides (genus), Oscillospiraceae (family), Ruminococcaceae (family), Bacteroidetes (family), Verrucomicrobiaceae (family), Coriobacteriaceae (family), Clostridiales (order), Bacteroideales (order), Verrucomicrobiales (order), Coriobacteriales (order), Thermoanaerobacterales (order), Clostridia (class), Bacteroidetes (class), Verrucomicrobiae (class), Verrucomicrobia (phylum), Bacteroidetes (phylum), and/or other suitable taxa (e.g., as described herein); and/or may include any combination of one or more of the following: Related functional features (e.g., features describing its abundance; features describing its relative abundance; features describing functional aspects associated with it; features describing features derived from it; features describing its presence and/or absence; etc.): transcription (KEGG2), cellular processes and signaling (KEGG2), amino acid metabolism (KEGG2), cell growth and death (KEGG2), replication and repair (KEGG2), metabolism of other amino acids (KEGG2), neurodegenerative diseases (KEGG2), metabolism of cofactors and vitamins (KEGG2), transport and catabolism (KEGG2), KEGG2), endocrine system (KEGG2), immune system diseases (KEGG2), excretion system (KEGG2), enzyme family (KEGG2), membrane transport (KEGG2), carbohydrate metabolism (KEGG2), biosynthesis of other secondary metabolites (KEGG2), metabolism of terpenoids and polyketides (KEGG2), infectious diseases (KEGG2), genetic information processing (KEGG2), nervous system (KEGG2), environmental adaptation (KEGG2), nucleotide metabolism (KEGG2), signaling molecules and interactions (KEGG2), G2), signal transduction (KEGG2), transport and metabolism of inorganic ions (KEGG3), chromosomes (KEGG3), cell cycle – Caulobacter (KEGG3), ribosome biogenesis (KEGG3), DNA replication proteins (KEGG3), transcription factors (KEGG3), glycine, serine and threonine metabolism (KEGG3), sulfur metabolism (KEGG3), other ion-coupled transporters (KEGG3), biosynthesis of valine, leucine and isoleucine (KEGG3), nitrogen metabolism (KEGG3), peptidoglycan biosynthesis ( KEGG3), homologous recombination (KEGG3), peroxisomes (KEGG3), sulfur relay system (KEGG3), peptidases (KEGG3), protein kinases (KEGG3), mismatch repair (KEGG3), xylene degradation (KEGG3), ribosomes (KEGG3), RNA polymerase (KEGG3), tryptophan metabolism (KEGG3), histidine metabolism (KEGG3), vitamin metabolism (KEGG3), cell motility and secretion (KEGG3), pyrimidine metabolism (KEGG3), cytoskeletal proteins (KEGG3), DNA Reproduction (KEGG3), amino sugar and nucleotide sugar metabolism (KEGG3), folate biosynthesis (KEGG3), carbon fixation in photosynthetic organisms (KEGG3), phosphatidylinositol signaling system (KEGG3), lysine degradation (KEGG3), selenium compound metabolism (KEGG3), fructose and mannose metabolism (KEGG3), inositol phosphate metabolism (KEGG3), protein folding and related processing (KEGG3), proteasome proliferator-activated receptor (PPAR) signaling pathway (KEGG3), lipid metabolism (KEGG3), valine, leucine leucine and isoleucine degradation (KEGG3), glyoxylate and dicarboxylic acid metabolism (KEGG3), arginine and proline metabolism (KEGG3), limonene and pinene degradation (KEGG3), D-alanine metabolism (KEGG3), porphyrin and chlorophyll metabolism (KEGG3), C5-branched dicarboxylic acid metabolism (KEGG3), molecular chaperones and folding catalysts (KEGG3), fatty acid metabolism (KEGG3), glutathione metabolism (KEGG3), pentose phosphate pathway (KEGG3), phosphotransferase system (KEGG3), system,PTS)(KEGG3), pantothenate and coenzyme A (CoA) biosynthesis(KEGG3), proximal tubule bicarbonate recovery(KEGG3), galactose metabolism(KEGG3), starch and sucrose metabolism(KEGG3), primary immunodeficiency(KEGG3), cysteine and methionine metabolism(KEGG3), ubiquinone and other terpenoid quinone biosynthesis(KEGG3), DNA repair and recombination proteins(KEGG3), tyrosine metabolism(KEGG3), phenylalanine, tyrosine and tryptophan biosynthesis(KEGG3), aminoacyl-tRNA biosynthesis(KEGG3), alanine, aspartate and glutamate metabolism(KEGG3), photosynthesis (KEGG3), other transporters (KEGG3), butyrate metabolism (KEGG3), bacterial secretion system (KEGG3), glycerophospholipid metabolism (KEGG3), oxidative phosphorylation (KEGG3), type I diabetes (KEGG3), glycolysis/gluconeogenesis (KEGG3), photosynthesis proteins (KEGG3), transporters (KEGG3), terpenoid skeleton biosynthesis (KEGG3), unsaturated fatty acid biosynthesis (KEGG3), signal transduction mechanism (KEGG3), ketone body synthesis and degradation (KEGG3), nucleotide excision repair (KEGG3), secretion system (KEGG3), Alzheimer's disease (KEGG3), zeatin biosynthesis synthesis (KEGG3), type II diabetes (KEGG3), D-glutamine and D-glutamate metabolism (KEGG3), taurine and hypotaurine metabolism (KEGG3), glutamatergic synapses (KEGG3), plant-pathogen interactions (KEGG3), vitamin B6 metabolism (KEGG3), citric acid cycle (TCA cycle) (KEGG3), ethylbenzene degradation (KEGG3), base excision repair (KEGG3), replication, recombination and repair proteins (KEGG3), ribosome biogenesis in eukaryotes (KEGG3), aminobenzoate degradation (KEGG3), bacterial movement proteins (KEGG3), biosynthesis of ansamycins (KEGG3), Ion channels (KEGG3), metabolism (KEGG2), poorly characterized (KEGG2), biosynthesis and biodegradation of secondary metabolites (KEGG3), lipoic acid metabolism (KEGG3), amino acid-related enzymes (KEGG3), transcription proteins (KEGG3), ascorbate and alginate metabolism (KEGG3), thiamine metabolism (KEGG3), unknown function (KEGG3), glycosaminoglycan degradation (KEGG3), others (KEGG3), pentose and glucuronide interconversion (KEGG3), biotin metabolism (KEGG3), phenylalanine metabolism (KEGG3), glycosphingolipid biosynthesis-ganglionic series (KEGG3), biosynthesis-ganglio series)(KEGG3), pore ion channels(KEGG3), membrane and intracellular structural molecules(KEGG3), purine metabolism(KEGG3), one carbon pool by folate(KEGG3), phosphate and hypophosphite metabolism(KEGG3), lysosome(KEGG3), drug metabolism-other enzymes(KEGG3), penicillin and cephalosporin biosynthesis(KEGG3), Huntington's disease(KEGG3), Nicotinate and nicotinamide metabolism(KEGG3), drug metabolism-cytochrome P450(KEGG3), lipopolysaccharide biosynthesis proteins(KEGG3), xenobiotic metabolism by cytochrome P450(KEGG3), tuberculosis(KEGG3), polycyclic aromatic hydrocarbon degradation(KEGG3), and/or any other suitable functional features (e.g., as described herein). In variations, characterization of a user may include characterizing the user as a person having one or more photosensitive skin-related conditions based on monitoring one or more of the above characteristics in a manner additional to or alternative to typical methods of diagnosis and/or treatment.

In a variation, performing the skin-related characterization process S135 may include performing a scalp-related condition characterization process for one or more scalp-related conditions. In one embodiment, the skin-related characterization process may be based on statistical analysis to identify a feature set with the highest correlation with a scalp-related condition, based on a random forest predictor algorithm trained with a training data set derived from a subset of the subject population and validated with a validation data set derived from a subset of the subject population, for which one or more therapies have a positive effect. In an embodiment, the scalp-related condition may include one or more of dandruff (e.g., characterized by flaking, itching, scaling of scalp skin, etc.) such as caused by dry skin, irritated oily skin, sensitivity to hair care products, and/or other suitable scalp-related conditions, other conditions that may lead to an imbalance in the scalp microbiome, and/or other scalp-related conditions. The scalp-related condition may be associated with a specific microbiome diversity and/or a health condition related to the relative abundance of intestinal microbes, microbes associated with any suitable physiological site, microbiome functional diversity, and/or other suitable microbiome-related aspects.

Microbiome signatures associated with (e.g., positively correlated; negatively correlated; useful for diagnosis; etc.) one or more scalp-related conditions (and/or other suitable skin-related conditions) can include signatures associated with any combination of one or more of the following taxa (e.g., signatures describing their abundance; signatures describing their relative abundance; signatures describing functional aspects associated with them; signatures describing derivations from them; signatures describing their presence and/or absence; etc.): Actinomycetes (class), Lactobacilliales (order), Actinomycetales (order), Firmicutes (phylum), Dermobacteriaceae (family), Lactobacillaceae (family), Propionibacteriumaceae (family), Corynebacterium (genus), Corynebacterium (genus), Propionibacterium (genus), Dermobacterium (genus), Eremococcus (genus), Corynebacterium freiburgense (species), Eremococcus kriklagensis (KEGG3) (KEGG4) (KEGG5) coleocola)(species), Corynebacterium species(species), Staphylococcus C9I2 species(species), Anaerobic cocci 8405254 species(species), Corynebacterium glucosides (species), Dermatobacterium hominis (species), Rhodomycetaceae(family), Enterobacteriaceae(family), Staphylococcaceae(family), Enterobacteriales(order), Bacillusales(order), Bifidobacterium(genus), Staphylococcus(genus), Atopobium(genus), Megasphaera(genus), Corynebacterium mastitis(species), Streptococcus BS35a species(species), Finegoldia magna(species), Staphylococcus aureus(species), Haemophilus influenzae(species), Corynebacterium NML 97-0186 species (species), oral taxa Streptococcus G59 species (species), Dora (genus), Rosebair 11SE39 species (species), Dora Lipostreptococcus (species), Prevotella (family), Veillonellaceae (family), Oscillospiraceae (family), Gram-negative bacteria (Negativicutes), Selenomonasales (order), Fingoldia (genus), Oscillospira (genus), Intestinimonas (genus), Flavonifractor (genus), Prevotella (genus), Moryella (genus), Catenibacterium mitsuokai (species), Collinsella aerogenes (genus), aerofaciens (species), Peptophilus 2002-2300004 (species), Corynebacterium canis (species), Fingoldella S9 AA1-5 (species), Prevotellabuccalis (species), Dialister invisus (species), Moraxella (genus), Neisseria (genus), Neisseria mucosa (species), mucosa (species), Rikenellaceae (family), and/or other suitable taxonomic groups (e.g., as described herein); and/or may include functional features (e.g., features describing its abundance; features describing its relative abundance; features describing functional aspects associated with it; features describing its derivation from it; features describing its presence and/or absence; etc.) associated with any combination of one or more of the following: metabolism of cofactors and vitamins (KEGG2), enzyme families (KEGG2), lipid metabolism (KEGG2), immune system diseases (KEGG2), glycolysis/gluconeogenesis (KEGG2), G3), primary immune deficiency (KEGG3), pyruvate metabolism (KEGG3), transport and catabolism (KEGG2), neurodegenerative diseases (KEGG2), endocrine system (KEGG2), amino acid metabolism (KEGG2), cellular processes and signaling (KEGG2), signaling molecules and interactions (KEGG2), metabolism of other amino acids (KEGG2), replication and repair (KEGG2), transcription (KEGG2), cell growth and death (KEGG2), membrane transport (KEGG2), biosynthesis of other secondary metabolites (KEGG2), terpenoids and polyketide Metabolism of compounds (KEGG2), transport and metabolism of inorganic ions (KEGG3), vitamin metabolism (KEGG3), biosynthesis of valine, leucine and isoleucine (KEGG3), peroxisomes (KEGG3), ribosome biogenesis (KEGG3), selenium compound metabolism (KEGG3), histidine metabolism (KEGG3), chromosomes (KEGG3), sulfur metabolism (KEGG3), PPAR signaling pathway (KEGG3), porphyrin and chlorophyll metabolism (KEGG3), phosphatidylinositol signaling system (KEGG3), inositol phosphate metabolism (KEGG3), sulfur relay system (KEGG3), glycine, serine and threonine metabolism (KEGG3), DNA replication proteins (KEGG3), pantothenate and CoA biosynthesis (KEGG3), transcription factors (KEGG3), protein folding and related processing (KEGG3), type II diabetes (KEGG3), protein kinases (KEGG3), folate biosynthesis (KEGG3), lysine degradation (KEGG3), RNA polymerase (KEGG3), D-alanine metabolism (KEGG3), carbon fixation in photosynthetic organisms (KEGG3), nitrogen metabolism (KEGG3), glycerophospholipid metabolism (KEGG3), Biosynthesis of ansamycins (KEGG3), Valine, Leucine and Isoleucine degradation (KEGG3), Cytoskeletal proteins (KEGG3), Peptidases (KEGG3), Fatty acid metabolism (KEGG3), Cell cycle - Caulobacter (KEGG3), Phosphotransferase system (PTS) (KEGG3), Pyrimidine metabolism (KEGG3), Alzheimer's disease (KEGG3), Butyrate metabolism (KEGG3), Tryptophan metabolism (KEGG3), Signal transduction mechanisms (KEGG3), Pentose phosphate pathway (KEGG3), Other ion-coupled transporters (KEGG3), Homologous recombination (KEGG3), replication, recombination and repair proteins (KEGG3), dimethylbenzene degradation (KEGG3), mismatch repair (KEGG3), glyoxylate and dicarboxylic acid metabolism (KEGG3), arginine and proline metabolism (KEGG3), peptidoglycan biosynthesis (KEGG3), molecular chaperones and folding catalysts (KEGG3), type I diabetes (KEGG3), DNA replication (KEGG3), bacterial secretion system (KEGG3), tyrosine metabolism (KEGG3), citric acid cycle (TCA cycle) (KEGG3), amino sugar and nucleotide sugar metabolism (KEGG3), ribosome (KEGG 3), limonene and pinene degradation (KEGG3), cell motility and secretion (KEGG3), taurine and hypotaurine metabolism (KEGG3), oxidative phosphorylation (KEGG3), fructose and mannose metabolism (KEGG3), vitamin B6 metabolism (KEGG3), ion channels (KEGG3), ketone body synthesis and degradation (KEGG3), other transporters (KEGG3), galactose metabolism (KEGG3), polycyclic aromatic hydrocarbons degradation (KEGG3), transporters (KEGG3), DNA repair and recombination proteins (KEGG3), starch and sucrose metabolism (KEGG3), alanine, aspartate and glutamate metabolism (KEGG3), ribosome biogenesis in eukaryotes (KEGG3), secretion system (KEGG3), biosynthesis of unsaturated fatty acids (KEGG3), cysteine and methionine metabolism (KEGG3), base excision repair (KEGG3), aminobenzoate degradation (KEGG3), photosynthesis (KEGG3), photosynthesis proteins (KEGG3), pore ion channels (KEGG3), lipid biosynthesis proteins (KEGG3), D-glutamine and D-glutamate metabolism (KEGG3), and/or any other suitable functional feature (e.g., as described herein, etc.). In a variation, characterization of a user can include characterizing the user as a person with one or more photosensitive skin-related conditions based on detecting one or more of the above features in a manner that is additional or alternative to typical methods of diagnosis and/or treatment.

However, determining one or more skin-related characteristics may be performed in any suitable manner.

4.4 Determine the treatment model

Method 100 may additionally or alternatively include block S140, which may include generating a therapy model configured to adjust the distribution of microorganisms in a subject characterized according to the characterization process. Block S140 may serve the following functions: identifying, sorting, prioritizing, determining, predicting, discouraging and/or otherwise promoting therapy determinations for therapies (e.g., probiotic-based therapies, phage-based therapies, small molecule-based therapies, etc.), such as therapies that can shift the composition and/or functional characteristics of the subject's microbiome (e.g., microbiome for any suitable site, etc.) toward a desired state of balance in promoting the health of the subject; and/or determining therapies for otherwise modifying the state of one or more microbial-related conditions (e.g., modifying user behavior associated with human behavior conditions, etc.). The microbial-related condition model may include one or more therapy models. In box S140, the therapy can be selected from the following therapies including one or more: probiotic therapy, phage-based therapy, small molecule-based therapy, cognitive/behavioral therapy, physical rehabilitation therapy, clinical therapy, drug-based therapy, diet-related therapy, and/or any other suitable therapy designed to operate in any other suitable manner to promote the health of the user. In a specific embodiment of a bacteriophage-based therapy, one or more populations of bacteriophages (e.g., expressed in colony forming units) specific to certain bacteria (or other microorganisms) represented in the subject can be used to downregulate or otherwise eliminate certain bacterial populations. In this way, bacteriophage-based therapy can be used to reduce the size of an undesirable population of bacteria represented in the subject. In addition, bacteriophage-based therapy can be used to increase the relative abundance of bacterial populations that are not targeted by the bacteriophage used.

In another specific embodiment of probiotic therapy, as shown in FIG4 , the candidate therapy of the therapy model can perform one or more of the following: prevent pathogens from entering epithelial cells by providing a physical barrier (e.g., by means of colonization resistance), induce the formation of a mucous barrier by stimulating goblet cells, enhance the integrity of apical tight junctions between epithelial cells of the subject (e.g., by stimulating upregulation of zona-occludens 1, by preventing redistribution of tight junction proteins), produce antimicrobial factors, stimulate the production of anti-inflammatory cytokines (e.g., through signaling of dendritic cells and induction of regulatory T cells), trigger an immune response, and perform any other suitable function to adjust the subject's microbiome out of a dysregulated state. In another specific embodiment, the therapy can include a medical device-based therapy (e.g., associated with human behavior modification, associated with the treatment of disease-related conditions, etc.).

In a variation, the therapy model is preferably based on data from a large subject population, which can be included in a subject population from which a microbiome diversity data set is derived in frame S110, wherein the microbiome composition and/or functional characteristics or health status before and after being fully exposed to various treatment measures are well characterized. These data can be used to train and verify the therapy in identifying treatment measures to provide a model, and the treatment measures provide the desired results for the subject based on different microorganisms. In a variation, as a supervised machine learning algorithm, a support vector machine can be used to generate a therapy to provide a model. However, any other suitable machine learning algorithm mentioned above can promote the generation of the therapy to provide a model.

Additionally or alternatively, the therapy model may be derived from the identification of a "normal" or baseline microbiome composition and/or functional characteristics assessed from a subject population identified as being physically healthy. Based on the identification of a subset of subjects characterized as being physically healthy (e.g., using features of a characterization process), a therapy that modulates the microbiome composition and/or functional characteristics toward the microbiome composition and/or functional characteristics of physically healthy subjects may be generated in block S140. Block S140 may therefore include identifying one or more baseline microbiome compositions and/or functional characteristics (e.g., one baseline microbiome for each of a demographic set), and potential therapeutic formulations and treatment regimens that may convert the microbiome of a subject in a dysbiotic state toward one of the identified baseline microbiome compositions and/or functional characteristics. However, the therapy model may be generated and/or improved in any suitable manner.

The microbial composition associated with the probiotic therapy associated with the allopathic model preferably includes microorganisms that are culturable (e.g., able to be expanded to provide scalable therapy) and non-lethal (e.g., non-lethal in their desired therapeutic dose). In addition, the microbial composition may include a single type of microorganism that has an acute effect or a moderate effect on the subject's microbiome. Additionally or alternatively, the microbial composition may include a balanced combination of multiple types of microorganisms that are configured to cooperate with each other in driving the subject's microbiome toward a desired state. For example, a combination of multiple types of bacteria in a probiotic therapy may include a first bacterial type that generates a product used by a second bacterial type, and the second bacterial type has a strong effect in actively affecting the subject's microbiome. Additionally or alternatively, a combination of multiple types of bacteria in a probiotic therapy may include several bacterial types that produce proteins with the same function, which positively affect the subject's microbiome.

The probiotic composition can be naturally derived or synthetically derived. For example, in one application, the probiotic composition can be naturally derived from fecal matter or other biological matter (e.g., from one or more subjects with a baseline microbiome composition and/or functional characteristics as identified using the characterization process and therapy model). Additionally or alternatively, the probiotic composition can be synthetically derived (e.g., derived using a benchtop method) based on the baseline microbiome composition and/or functional characteristics as identified using the characterization process and therapy model. In variations, microbial agents useful in probiotic therapy may include one or more of yeast (e.g., Saccharomyces boulardii), Gram-negative bacteria (e.g., E. coli Nissle), Gram-positive bacteria (e.g., Bifidobacteria bifidum, Bifidobacteria infantis, Lactobacillus rhamnosus, Lactococcus lactis, Lactobacillus plantarum, Lactobacillus acidophilus, Lactobacillus casei, Bacillus polyfermenticus, etc.), and any other suitable type of microbial agent.

In one variation, the therapy may include a probiotic therapy for one or more skin-related conditions (e.g., for improving a health state associated with one or more skin-related conditions; etc.), wherein the probiotic therapy may include any one or more of the following in combination: Corynebacterium ulcerans, Fecklumella hominis, Corynebacterium species, Propionibacterium species MSP09A, Fecklumella species 1440-97, Staphylococcus aureus C9I2, Anaerobic cocci species 9402080, Corynebacterium glucosinolates, Bacillus hominis, Lactobacillus species BL302, Corynebacterium mastitis, Bifidobacterium longum, Anaerobic cocci species, Anaerobic cocci species S9 PR-16, Prevotella timoniensis, Kluyveromyces georginae, Acinetobacter WB22-23, Anaerobic cocci of octaci, Fingoldella S9AA1-5, Staphylococcus C-D-MA2, Peptophilus 7-2, Cronobacter sakazakii, Anaerobic cocci 8405254, Veillonella CM60, Lactobacillus 7_1_47FAA, Gemini 933-88, Porphyromonas cardiogensis, Haemophilus parainfluenzae, Bacteroides AR20, Bacteroides vulgaris, Bacteroides D22, Streptococcus dorae, Parabacteroides faecalis, Bacteroides AR29, Prevotella WAL 2039G, Faecalibacterium prausnitzii, Blautus fecal, Bacteroides putrefaciens, Bacteroides acidophilus, Adler Kreutz, Pseudomonas succinici, Roseborgia glucosphaeria, Pseudomonas 377, Desulfovibrio pilosulatus, Eggertella HGA1, Lactobacillus longum, Bacteroides HGB5, Holdermanella filamentosa, Collinsella enterica, Neisseria macaques (Neisseria macacae), Geminis, Bacteroides fragilis, Prevotella oralis, Pseudomonas brenner, Flavobacterium ceti, Brevundimonas FXJ8.080, Bacteroides plebius, Curvularia cambriana, Wexlera, Staphylococcus WB18-16, Oral taxa Streptococcus G63, Propionibacterium acnes, Anaerobic cocci 9401487, Staphylococcus epidermidis, Campylobacter urealyticum, Janus M3-5, Peptophilus DNF00840, Fingoldiella S8 F7, Prevotella saccharolyticus, Fusobacterium periodontalis, Corynebacterium freiburgii, Krickla, Streptococcus BS35a, Fingoldiella major, Staphylococcus aureus, Haemophilus influenzae, Corynebacterium NML 97-0186, oral taxon Streptococcus G59, Roseborgia 11SE39, Streptobacillus mitsuoka, Collinsella aerogenes, Peptophilus 2002-2300004, Corynebacterium canis, Prevotella oralis, Dialisterella opacus, Neisseria mucoides, and/or any other suitable microorganism of any suitable taxonomic group (e.g., described herein) and/or phage vector (e.g., bacteriophage, virus, etc.). In a specific embodiment, the probiotic therapy and/or other suitable probiotic therapy can be promoted (e.g., recommended; otherwise provided; etc.) at a dose of 100,000 to 10 billion colony-forming units (CFU), as determined from a therapy model that predicts a positive adjustment of the patient's microbiome in response to therapy. In embodiments, a subject may be instructed to ingest capsules comprising a probiotic formulation according to a regimen customized for one or more of his/her physiology (e.g., body mass index, weight, height), demographics (e.g., sex, age), severity of dysbiosis, sensitivity to medications, and/or any other suitable factors.

In one variation, for a subject exhibiting one or more skin-related conditions, including one or more photosensitivity-related conditions, dry skin-related conditions, scalp-related conditions, and/or other suitable skin-related conditions, microorganisms associated with the skin-related conditions can provide a dataset based on the composition or diversity of recognizable patterns of relative abundance among the microorganisms present in the subject's microbiome, and the microorganisms associated with the skin-related conditions can be used as a diagnostic tool and/or a therapeutic tool using the bioinformatics pipelines and/or characterizations described above.

In another variation, a microbial dataset (e.g., composition or diversity based on patterns of relative abundance identifiable in the microorganisms present in a subject's microbiome) can be used as a diagnostic tool using a bioinformatics pipeline and the characterization described above. However, probiotic therapy and/or other suitable therapies can include any suitable combination of microorganisms associated with any suitable taxonomic group described herein.

Probiotics and/or other suitable consumables can be provided at a dose of 100,000 to 10 billion CFU (and/or other suitable doses), for example as determined from a therapy model that predicts a positive adjustment of a patient's microbiome in response to therapy. In a particular embodiment, a subject can be instructed to ingest a capsule containing a probiotic formulation according to a regimen customized for one or more of his/her: physiology (e.g., body mass index, weight, height), demographics (e.g., sex, age), severity of dysbiosis, sensitivity to drugs, and/or any other suitable factors. For subjects exhibiting microbial-related conditions, associated microorganisms (e.g., corresponding to associated microbiome composition features) can provide a dataset based on the composition or diversity of recognizable patterns of relative abundance in microorganisms present in the subject's microbiome, and can be used as a diagnostic tool using a bioinformatics pipeline and the characterization described above.

4.5 Processing of User Biological Samples

Method 100 may additionally or alternatively include block S150, which may include processing one or more biological samples from a user (e.g., biological samples from different collection sites of a user, etc.). Block S150 may function to facilitate the generation of a microbial dataset for a subject, such as for use in deriving input for a characterization process (e.g., for use in generating microbial-related characterizations for a user, such as by applying one or more microbiome characterization modules, etc.). Thus, block S150 may include receiving, processing, and/or analyzing one or more biological samples from one or more users (e.g., multiple biological samples for the same user over time, different biological samples for different users, etc.). In block S150, the biological sample is preferably generated from the subject and/or the subject's environment in a non-invasive manner. In variations, the non-invasive manner of sample reception may use any one or more of the following: a permeable substrate (e.g., a swab, toilet paper, sponge, etc. configured to wipe a body area of a subject), an impermeable substrate (e.g., a slide, tape, etc.), a container configured to receive a sample from a body area of a subject (e.g., a medicine bottle, a test tube, a bag, etc.), and any other suitable sample receiving element. In a particular embodiment, a biological sample may be collected from one or more of the nose, skin, genitals, mouth, and intestine of a subject in a non-invasive manner (e.g., using a swab and a medicine bottle). However, the biological sample may additionally or alternatively be received in a semi-invasive manner or in an invasive manner. In variations, the invasive manner of sample reception may use any one or more of the following: a needle, a syringe, a biopsy element, a lancet, and any other suitable instrument for collecting a sample in a semi-invasive or invasive manner. In particular embodiments, the sample may include a blood sample, a plasma/serum sample (e.g., to enable extraction of cell-free DNA), and a tissue sample.

In the above variations and embodiments, biological sample can be taken from the body of the subject without the promotion of other entities (e.g., caregivers, healthcare professionals, automatic or semi-automatic sample collection devices, etc.) or can be taken from the body of the subject with the assistance of other entities. In one embodiment, wherein the biological sample is taken from the subject without the promotion of other entities during the sample extraction process, the sample provides a test kit that can be provided to the subject. In this embodiment, the test kit may include one or more swabs for sample acquisition, one or more containers configured to receive the swab for storage, sample provision and user account settings, elements configured to associate the sample with the subject (e.g., barcode identifiers, labels, etc.) and allow the sample from the subject (e.g., by a mail delivery system) to be delivered to the receiver (receptacle) of the sample processing operation. In another embodiment, wherein the biological sample is extracted from the subject with the help of other entities, one or more samples can be collected from the subject in a clinical or research setting (e.g., during a clinical appointment). However, the biological sample can be received from the subject in any other suitable manner.

In addition, processing and analyzing a biological sample from a subject (e.g., to generate a user microbial data set, etc.) is preferably performed in a manner similar to one of the embodiments, variations, and/or examples of sample reception described above with respect to block S110, and/or any other suitable portion of method 100. Thus, the receiving and processing of the biological sample in block S150 can be performed for the subject using a process similar to the process used to receive and process the biological sample, which is used to generate the characterization process and/or therapy model of method 100, so as to provide consistency of process. However, the receiving and processing of the biological sample in block S150 may alternatively be performed in any other suitable manner.

4.6 Determination of relevant characteristics of microorganisms

Method 100 may additionally or alternatively include block S160, which may include: determining microbe-related characterizations for a user using a characterization process, such as based on processing one or more microbe data sets derived from a biological sample of the user (e.g., a user microbe sequence data set, a microbe group composition data set, a microbe group functional diversity data set; processing of a microbe data set to extract microbe group features; etc.). Block S160 may function to characterize one or more microbe-related conditions for a user, such as by extracting features from the subject's microbe group-derived data and using the features as inputs in the embodiments, variations, or embodiments of the characterization process described above in block S130 (e.g., using user microbe group feature values as inputs in a microbe group-related condition characterization model, etc.). In an embodiment, block S160 may include generating microbe-related characterizations for a user (e.g., generated in block S130) based on the user's microbe group features and the microbe-related condition characterization model. The microbe-related characterizations may be for any number and/or combination of microbe-related conditions (e.g., a combination of microbe-related conditions, a single microbe-related condition, and/or other suitable microbe-related conditions, etc.). The microbe-related representations may include one or more of the following: diagnosis (e.g., the presence or absence of a microbe-related condition; etc.); risk (e.g., a risk score for the development and/or existence of a microbe-related condition; information about the microbe-related representation (e.g., symptoms, signs, triggers, associated conditions, etc.); comparison (e.g., comparison with other subgroups, populations, users, historical health states of users such as historical microbiome composition and/or functional diversity; comparisons associated with microbe-related conditions; etc.), and/or any other suitable data.

In another variation, the microbe-related characterization may include a microbiome diversity score (e.g., regarding microbiome composition, function, etc.) associated with (e.g., correlated with; negatively correlated with; positively correlated with; etc.) a microbiome diversity score that is associated with one or more microbiome-related conditions. In an embodiment, the microbiome-related characterization may include a microbiome diversity score over time (e.g., calculated for a plurality of biological samples of a user collected over time), a comparison with a microbiome diversity score of other users, and/or any other suitable type of microbiome diversity score (e.g., determining a microbiome diversity score; using a microbiome diversity score to determine and/or provide a therapy; etc.) may be performed in any suitable manner.

Determining microbe-related characterizations in box S160 preferably includes identifying features and/or combinations of features associated with the subject's microbiome composition and/or functional characteristics, inputting the features into a characterization process, and receiving outputs that characterize the subject as belonging to one or more of the following: behavioral groups, gender groups, dietary groups, disease state groups, and any other suitable groups that can be identified by the characterization process. Box S160 may additionally or alternatively include the generation and/or output of a confidence indicator associated with the subject's characterization. For example, the confidence indicator may be derived from: the number of features used to generate the characterization, the relative weight or ranking of the features used to generate the characterization, a deviation measurement in the characterization process, and/or any other suitable parameter associated with an aspect of the characterization process. However, utilizing user microbiome features may be performed in any suitable manner to generate any suitable microbe-related characterization.

In some embodiments, features extracted from a subject's microbiome dataset may be supplemented with supplemental features (e.g., extracted from supplemental data collected for a user; such as survey-derived features, medical history-derived features, sensor data, etc.), where these data, user microbiome data, and/or other suitable data may be used to further refine the characterization process of box S130, box S160, and/or other suitable portions of method 100.

Determining microbiome-related characterizations preferably includes extracting and applying microbiome features (e.g., user microbiome composition diversity features; user microbiome functional diversity features; etc.) for a user (e.g., based on a user microbiome dataset), characterization models, and/or other suitable components, such as by employing the method described in box S130, and/or by employing any suitable method described herein.

In a variation, as shown in FIG6 , block S160 may include, for example, presenting microbial-related representations (e.g., information extracted from the representations, etc.) at a web interface, a mobile application, and/or any other suitable interface, but the presentation of the information may be performed in any suitable manner. However, the subject's microbial dataset may additionally or alternatively be used in any other suitable manner to enhance the model of method 100, and block S160 may be performed in any suitable manner.

4.7 Facilitating therapeutic interventions

As shown in FIG. 9 , method 100 may additionally or alternatively include block S170, which may include promoting therapeutic interventions for one or more microbial-related conditions for one or more users (e.g., promoting therapy, providing therapy, promoting the provision of therapy, etc.) (e.g., based on the microbial-related representations and/or therapy models). Block S170 may function to recommend, promote, provide, and/or otherwise promote therapeutic interventions for one or more therapies for the user, such as shifting the user's microbiome composition and/or functional diversity toward a desired equilibrium state for one or more microbial-related conditions (and/or otherwise improving the state of the microbial-related conditions, etc.). Block S170 may include providing a customized therapy to a subject based on the subject's microbiome composition and functional characteristics, wherein the customized therapy may include a microbial formulation configured to correct the dysbiosis characteristics of the subject having the identified representations. Thus, based on the trained therapy model, the output of block S140 may be used to promote customized therapy formulations and regimens (e.g., dosages, instructions for use) directly to the subject. Additionally or alternatively, therapy provision may include recommending available therapeutic measures that are configured to transform the microbiome composition and/or functional characteristics toward a desired state. In variations, therapy may include any one or more of the following: consumables, topical therapies (e.g., lotions, ointments, preservatives, etc.), drugs (e.g., drugs associated with any suitable drug type and/or dosage, etc.), bacteriophages, environmental treatments, behavioral changes (e.g., dietary change therapy, stress reduction therapy, physical activity-related therapy, etc.), diagnostic procedures, other medical-related procedures, and/or any other suitable therapy associated with a microbial-related condition. Consumables may include any one or more of the following: food and/or beverages (e.g., probiotics and/or prebiotic foods and/or beverages, etc.), nutritional supplements (e.g., vitamins, minerals, fibers, fatty acids, amino acids, prebiotics, probiotics, etc.), consumable drugs, and/or any other suitable therapeutic measures.

For example, based on the output of the therapy model, a combination of commercially available probiotic supplements may include a suitable probiotic therapy for the subject. In another embodiment, method 100 may include determining a risk of a microbe-related condition for a user for the microbe-related condition based on the microbe-related condition model (e.g., and/or a user microbiome signature); and promoting a therapy to the user based on the risk of the microbe-related condition.

In one variation, promoting a therapy may include promoting a diagnostic procedure (e.g., for promoting detection of microbial-related conditions such as human behavior conditions and/or disease-related conditions, which may stimulate subsequent promotion of other therapies, such as for regulating a user's microbiome to improve a user's health state associated with one or more microbial-related conditions; etc.). The diagnostic procedure may include any one or more of the following: medical history analysis, imaging examination, cell culture test, antibody test, skin prick test, patch test, blood test, challenge test, performing a portion of method 100, and/or any other suitable procedure for promoting detection (e.g., observation, prediction, etc.) of a microbial-related condition. Additionally or alternatively, diagnostic device-related information and/or other suitable diagnostic information may be processed as part of a supplemental data set (e.g., with respect to block S120, where such data may be used in determining and/or applying a characterization model, a therapy model, and/or other suitable model; etc.), and/or collected, used, and/or otherwise processed with respect to any suitable portion of method 100 (e.g., administering a diagnostic procedure to a user to monitor therapy efficacy with respect to block S180, etc.).

In another variation, frame S170 can include promoting bacteriophage-based therapy. In more detail, one or more populations (e.g., expressed in colony forming units) of bacteriophages specific to certain bacteria (or other microorganisms) represented in the subject can be used to downregulate or otherwise eliminate certain bacterial populations. In this way, bacteriophage-based therapy can be used to reduce the size of undesirable populations of bacteria represented in the subject. In addition, bacteriophage-based therapy can be used to increase the relative abundance of bacterial populations that are not targeted by the bacteriophages used.

In another variation, the therapy provision in block S170 may include providing notifications to the subject about recommended therapies, other forms of therapy, microbe-related representations, and/or other suitable data. In a particular embodiment, providing therapy to a user may include: providing therapy recommendations and/or other suitable therapy-related information (e.g., efficacy; comparisons with other individual users, user subgroups, and/or user groups; therapy comparisons; historical therapy and/or associated therapy-related information; such as psychotherapy guidelines for cognitive behavioral therapy; etc.), such as by displaying a notification on a web interface (e.g., by associating with a user and identifying a user's user account; etc.), (e.g., substantially simultaneously with providing information derived from microbe-related representations to the user, etc.). Notifications may be provided to a subject by an electronic device (e.g., a personal computer, a mobile device, a tablet, a wearable, head-mounted wearable computing device, a wrist-mounted wearable computing device, etc.) that executes an application, a web interface, and/or a message client configured to provide notifications. In one embodiment, a web interface of a personal computer or portable computer associated with a subject can provide access to a user account of the subject through the subject, wherein the user account includes information about the user's microbial-related condition, specific characterizations of aspects of the user's microbiome (e.g., about correlations with microbial-related conditions; etc.), and/or notifications about recommended therapeutic actions (e.g., generated in blocks S140 and/or S170). In another embodiment, an application executed in a personal electronic device (e.g., a smart phone, a smart watch, a head-mounted smart device) can be configured to provide notifications (e.g., on a display, haptically, audibly, etc.) about therapeutic recommendations generated by the therapy model of block S170. Additionally or alternatively, notifications and/or probiotic therapies can be provided directly through an entity associated with the subject (e.g., a caregiver, a spouse, a significant other, a healthcare professional, etc.). In some other variations, the notification may additionally or alternatively be provided to an entity associated with the subject (e.g., a healthcare professional, etc.), such as where the entity is able to facilitate the provision of therapy (e.g., by way of prescription, by way of guiding the therapy session via a digital telemedicine session using the computing device's optical and/or acoustic sensors, etc.). However, promoting the notification and/or other appropriate therapy may be performed in any suitable manner.

4.8 Monitoring the efficacy of therapy

As shown in Figure 7, the method may additionally or alternatively include a frame S180, which lists: based on processing biological samples, monitoring the effectiveness of the therapy to the subject, to evaluate the composition and/or functional characteristics of the microbiome at the time point set associated with the probiotic therapy for the user. Frame S180 can play a role in aggregating additional data, which is about the positive effects, negative effects and/or lack of effectiveness of the probiotic therapy suggested by the subject therapy model of the given characterization. Therefore, during the treatment process promoted by the therapy model (e.g., by receiving and analyzing biological samples from the subject throughout the treatment, by receiving survey-derived data from the subject throughout the treatment), the monitoring of the subject can be used for each characterization provided by the characterization process of frame S130, and each recommended treatment provided in frame S140 and S170, Generate a therapy effectiveness model.

In block S180, the subject may be prompted to provide additional biological samples at one or more key time points of the treatment regimen incorporating the therapy, and the additional biological samples may be processed and analyzed (e.g., in a manner similar to that described with respect to block S120) to generate indicators characterizing the modulation of the composition and/or functional characteristics of the subject's microbiome. For example, indicators related to one or more of the following and/or any suitable indicators may be used to assess the effectiveness of the therapy from changes in the composition and/or functional characteristics of the microbiome: changes in the relative abundance of one or more taxonomic groups represented in the subject's microbiome at early time points, changes in a specific taxonomic group representing the subject's microbiome, the ratio between the abundance of bacteria in the first taxonomic group and the abundance of bacteria in the second taxonomic group of the subject's microbiome, changes in the relative abundance of one or more functional families in the subject's microbiome. Additionally or alternatively, survey-derived data from the subject's experience with the subject during treatment (e.g., side effects experienced, improved personal assessments, behavioral changes, symptom improvements, etc.) may be used to determine the effectiveness of the therapy in block S180. For example, method 100 may include: receiving a post-treatment biological sample from a user; collecting a supplemental data set from the user, wherein the supplemental data set describes the user's compliance with a therapy (e.g., a determined and promoted therapy) and/or other suitable user characteristics (e.g., behavior, condition, etc.); generating a post-treatment microbe-related characterization of a first user for a microbe-related condition based on a microbe-related condition characterization model and the post-treatment biological sample; and promoting an updated therapy for the microbe-related condition to the user based on the post-treatment microbe-related characterization (e.g., based on a comparison between the post-treatment microbe-related characterization and the pre-treatment microbe-related characterization; etc.) and/or the user's compliance with the therapy (e.g., changing the therapy based on a positive or negative result of the user's microbiome for the microbe-related condition; etc.). Additionally or alternatively, other suitable data (e.g., supplemental data describing user behavior related to human behavior conditions; supplemental data describing disease-related conditions such as observed symptoms; etc.) may be used in determining a post-treatment characterization (e.g., the extent of change from pre-treatment to post-treatment for the microbe-related condition, etc.), an updated therapy (e.g., determining an updated therapy based on effectiveness and/or compliance with the promoted therapy, etc.). Therapy effectiveness, processing of additional biological samples (e.g., to determine additional microbe-related characterizations, therapies, etc.), and/or other suitable aspects associated with continuous biological sample collection, processing, and analysis of microbe-related conditions can be performed at any suitable time and frequency to generate, update, and/or otherwise process models (e.g., characterization models, therapy models, etc.), and/or for any other suitable purpose (e.g., as input associated with other portions of method 100). However, block S180 can be performed in any suitable manner.

However, method 100 may include any other suitable blocks or steps configured to facilitate receiving a biological sample from a subject, processing the biological sample from the subject, analyzing data derived from the biological sample, and generating a model that can be used to provide customized diagnostics and/or probiotic-based therapies based on the subject's specific microbiome composition and/or functional characteristics.

Embodiments of the systems and/or methods may include every combination and permutation of the various system components and the various method processes, including any variations, embodiments, and specific embodiments, wherein the methods and/or processes described herein may be performed asynchronously (e.g., sequentially), simultaneously (e.g., in parallel), or in any other suitable order by and/or using one or more instances of the systems, elements, and/or entities described herein.

Any of the variations described herein (e.g., embodiments, variations, examples, specific examples, illustrations, etc.) and/or any portion of the variations described herein may be additionally or alternatively combined, excluded, and/or applied in other ways.

The system and method and embodiments thereof may be at least partially embodied and/or implemented as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions are preferably executed by a computer-executable component, which is preferably integrated with the system. The computer-readable medium may be stored on any suitable computer-readable medium, such as RAM, ROM, flash memory, electrically erasable read-only memory (EEPROM), optical devices (CD or DVD), hard drives, floppy disk drives, or any suitable device. The computer-executable component is preferably a general-purpose or special-purpose processor, but any suitable special-purpose hardware or hardware/firmware combination device may alternatively or additionally execute the instructions.

As those skilled in the art will recognize from the previous detailed description and from the accompanying drawings and claims, modifications and changes may be made to the embodiments without departing from the scope defined in the appended claims.

Table 1.

Table 2.

Table 3

Table 4

Table 5

Table 6

Table 7

Table 8

Table 9

Table 10

Table 11

Table 12

Table 13

Table 14

Table 15

Table 16

Table 17

Table 18

Table 19

Table 20

Table 21

Table 22

Table 23

Taxonomic group ID Category Name 544 Citrobacter 817 Bacteroides fragilis 872 Desulfovibrio 901 Desulfovibrio piglensis 28118 Effeminate bacteria 28221 Delta-Proteobacteria 35832 Bilephila 35833 Bilophila wollastonii 47678 Bacteroides faecalis 100883 Faecalibacterium 194924 Desulfovibrioaceae 213115 Desulfovibrioles 216572 Oscillospiraceae 283168 Oleobacter 292800 Fusobacterium prausnitzii 310298 Bacteroides faecalis 408103 Citrobacter rodentium BW4 species 447027 Another strain EBA6-25cl2 556262 Faecalibacterium species D6 650643 Another branch of RMA 9912 species 693988 Bilephila 4_1_30 species 946234 Freifeldtella

Table 24

Table 25

Table 26

Table 27

symptom

Table 28

Celiac disease Gluten intolerance Wheat Allergy Celiac disease 1827 Gluten intolerance 1796 1818 Wheat Allergy 751 740 905

Table 29

Dairy Allergy Lactose intolerance Dairy Allergy 415 Lactose intolerance

Table 30

Table 31

Number of conditions female% male% 0 xx Xx 1 27.3 24.7 2 22.3 16.8 3 17.2 9.9 4 11.9 5.0 5 7.9 3.6 6 4.6 1.5 7 3.4 1.2 8+ 4.3 1.2

Claims (15)

1. A system for the characterization of microbial-related conditions, said system comprising: A sample processing system comprising a sequencing system operable to determine a microbial genetic sequence based on a sample associated with a collection of subjects, wherein the sample includes microbial nucleic acid associated with the microbial-related condition, The microbial nucleic acid is derived from a site diversity sample collected from a plurality of collection sites, including at least two of the intestine, genitals, mouth, skin and nose; A set of microbiome characterization modules operable to apply a set of analysis techniques, the set of analysis techniques including at least two of statistical testing, dimensionality reduction techniques, and artificial intelligence methods, and wherein the set of microbiome characterization modules includes: A first microbiome characterization module operable to apply a first analytical technique of the set of analytical techniques to determine a set of microbiome characteristics based on the microbial genetic sequence, wherein the set of microbiome characteristics is associated with the microbial-related condition. couplet; and A second microbiome characterization module operable to apply a second analysis technique from the set of analysis techniques to determine a processed set of microbiome characteristics based on the set of microbiome characteristics, wherein the processed set of microbiome characteristics is appropriate To improve the characterization of conditions associated with said microorganism; and A microbial-associated condition model generated based on the processed set of microbiome features, wherein the microbial-associated condition model is operable to determine a representation of the microbial-associated condition for a user, the characterization comprising based on the site diversity A set of site-wise disease susceptibility indicators for the sample collection and the microorganism-related condition model, wherein each site-side disease susceptibility indicator in the set of site-based disease susceptibility indicators corresponds to the plurality of collections Different collection sites of sites are associated with the microbial-related conditions. 2. The system of claim 1, wherein the first analysis technique includes a statistical test including at least one of a t-test, a Kolmogorov-Smirnov test, and a regression model, and wherein said first microbiome characterization module is operable to apply said statistical test to determine said set of microbiome characteristics based on said microbial genetic sequence. 3. A system according to any one of claims 1 and 2, wherein the first microbiome characterization module is operable to apply the statistical test to determine a first subset of microbiome features of the set of microbiome features based on the locus diversity sample, wherein from the first microorganism Each microbiome feature subset of the set feature subsets corresponds to a different collection site from the plurality of collection sites, wherein the second microbiome characterization module is operable to apply additional statistical tests to determine a second subset of microbiome features of the set of microbiome features based on the locus diversity sample, and The microorganism-related condition model is generated based on the first microbiome feature subset and the second microbiome feature subset. 4. The system of any one of claims 1 and 2, wherein the second microbiome characterization module is operable to apply the second analysis technique to perform at least one of feature selection, feature weighting, and warm starting. An item for processing the microbiome feature set into the processed microbiome feature set. 5. The system of claim 1, wherein the microbiome-related condition model includes a skin-related characterization model generated based on the processed set of microbiome features, wherein the skin-related characterization model is operable to determine for the user Said characterization of a photosensitivity-related condition, and wherein said set of microbiome features includes features associated with at least one of: Pseudomonas spp. (genus), Prevotella sp. WAL 2039G (species), mastitis Corynebacterium (species), Bacteroidetes (family), Blautia (genus), Bacteroides (genus), Desulfovibrio (genus), Clostridium (genus), Bacteroides vulgaris (species) , Faecalibacterium prausnitzii (species), Blautia faecalis (species), Mycobacterium putrificans (species), Bacteroides AR20 species (species), Bacteroides AR29 species (species), Bacteroides acidogenum (species) , Dilmaella (genus), Slyckella (genus), Eggertella (genus), Adlerkreuzia (genus), Paraprevotella (genus), Altomycota (genus), Haldemanella (genus), Essenbergia (genus), Enterobacter (genus), Adlerkreutzia species (species) , Coolabacillus succinic acidus (species), Rosebaryllium glucovorans (species), Coracobacterium 377 species (species), Desulfovibrio piagellani (species), Eggertella HGA 1 species (species), Endobacterium Lipokine longform Lactobacillus sp. (species), Mycobacterium sp. HGB5 (species), Haldemanella filamentum (species), Collinsella enterica (species), Neisseria macaque (species), Clostridium Bacteriaceae (family), Haemogeminis (species), Bacteroides fragilis (species), Enterobacteriaceae (family), Lachnospiraceae (family), Pasteurellaceae (family), Pasteurellales ( order), Enterobacteriaceae (order), Sphingobacteriaceae (order), Haemophilus (genus), Leuconostoc (genus), Brevundumonas (genus), Prevotella oralis ( species), Odoribacter spp. (genus), Capnocytophaga spp. (genus), Flavobacterium spp. (genus), Pseudomonas brennerensis (species), Xanthomonas cetaceans (species), Brevundumonas FXJ8 .080 species (species), Ruminococcaceae (family), Vibrionaceae (family), Flavobacteriaceae (family), Fusobacteriaceae (family), Porphyromonadaceae (family), Brevibacteriaceae (family) , Rhodobacteriaceae (family), Intersporaceae (family), Bifidobacteriaceae (family), Sphingobacteriaceae (family), Caulobacteriaceae (family), Campylobacteriaceae (family), Bacteroidetes Class (class), Class Fusobacteria (class), Class Flavobacteria (class), Bifidobacteriales (order), Neisseriales (order), Bacteroidetes (order), Rhodobacteriales (order), Flavobacteriales (order), Bacilliales (order), Vibrionales (order), Fusobacteriaceae (order), Caulobacteriales (order), Fusobacteriaceae (phylum), Actinobacteria (genus), Campylobacter (genus) , Streptobacterium (genus), Brevibacterium (genus), Faecalibacterium (genus), Campylobacter (genus), Actinobacterium (genus), Porphyromonas (genus), Fusobacterium (genus), Chryseobacterium (genus), Megacoccus (genus), Roseburia (genus), Neisseria (genus), Lactobacillus BL302 species (species), Bacteroides prebius (species ), Corynebacterium ulcerans (species), Campylobacter Cambrianii (species), Blautia wexleri (species), Staphylococcus WB18-16 species (species), Oral taxon Streptococcus species G63 (species) , Propionibacterium acnes (species), Anaerobic cocci 9401487 species (species), Haemophilus parainfluenzae (species), Staphylococcus epidermidis (species), Campylobacter urealyticum (species), Janus species M3-5 (species), Prevotella timoniniensis (species), Peptonephila sp. NF00840 (species), Fingoldella sp. S8 F7 (species), Prevotella glycopeptone (species), Porphyrin cassava Chloromonas (species), Fusobacterium periodontium (species), infectious diseases KEGG2, poorly characterized KEGG2, metabolic diseases KEGG2, immune system diseases KEGG2, cellular processes and signaling KEGG2, restriction endonuclease KEGG3, nuclear Repair KEGG3 by nucleotide excision, or wherein said microbiome-related condition model includes a skin-related representation model generated based on said processed set of microbiome features, wherein said skin-related representation model is operable to determine a representation of a skin dryness-associated condition for said user, and wherein said The microbiome feature set includes features related to at least one of the following: Corynebacteriaceae (family), Bacillus (class), Lactobacilliles (order), Actinomycetes (order), Firmicutes (phylum) ), Corynebacterium (genus), Dermobacteriaceae (family), Lactobacilliaceae (family), Propionibacteriaceae (family), Actinobacteria (class), Dermobacteriaceae (genus), Dialistae Bacillus (genus), Fikerum (genus), Lactobacillus (genus), Propionibacterium (genus), Corynebacterium ulcerans (species), Fikerum (species), Corynebacterium (species) species), Propionibacterium sp. MSP09A (species), Fecklamia sp. 1440-97 (species), Staphylococcus sp. C9I2 (species), Anaerobic cocci 9402080 species (species), Corynebacterium glucosinolyticus (species) , Dermatobacterium hominis (species), Enterobacteriaceae (family), Pseudomonadaceae (family), Staphylococcus (family), Gammaproteobacteria (class), Bacillales (order), Enterobacteriaceae (Order), Bifidobacterium (genus), Pseudomonas (genus), Anaerobicococcus (genus), Kluyveria (genus), Atopia (genus), Staphylococcus spp. (genus), Lactobacillus BL302 species (species), Corynebacterium mastitis (species), Bifidobacterium longum (species), Gemini anaerobic cocci species (species), Anaerobic cocci S9 PR-16 species (species), Phytophthora Prevotella monin (species), Kluyveromyces georgina (species), Actinomyces (genus), Fingoldia (genus), Cronobacter (genus), Acinetobacter B22-23 species (species), Anaerobic cocci Sarcina (species), Fingoldella S9 AA1-5 species (species), Staphylococcus aureus C-D-MA2 species (species), Peptonephila 7-2 species (species) ), Cronobacter sakazakii (species), Pasteurellaceae (family), Acidobacteriaceae (class), Sphingobacteriaceae (class), Sphingobacteriaceae (order), Acidobacteriaceae ( Phylum), Porphyromonas (genus), Haemophilus (genus), Acinetobacter (genus), Anaerobic cocci 8405254 species (species), Sphingomonadiaceae (family), Sphingomonas Alcoholomonadales (order), Cookella (genus), Geminicoccus (genus), Veillonella CM60 species (species), Lactobacillus 7_1_47FAA species (species), Geminicoccus 933-88 species (species) , Porphyromonas cassis (species), Haemophilus parainfluenzae (species), Bacteroides AR20 species (species), Bacteroides vulgaris (species), Bacteroides species D22 (species), Streptomyces multielongata Bacteroidetes (species), Parabacteroides faecalis (species), Bacteroidetes AR29 species (species), Doractella (genus), Collinsella (genus), Bacteroidetes (genus), Oscillatoryspirillaceae (family ), Ruminococcaceae (family), Bacteroidetes (family), Verrucomicrobiaceae (family), Rhodobacteriaceae (family), Clostridiales (order), Bacteroidetes (order), Verrucomicrobiales (order), Rhododendrones (order), Thermoanaerobes (order), Clostridiales (class), Bacteroidetes (class), Verrucomicrobia (class), Verrucomicrobia (phylum) , Bacteroidetes (phylum), Transcription KEGG2, Cellular processes and signaling KEGG2, Amino acid metabolism KEGG2, Cell growth and death KEGG2, Replication and repair KEGG2, Metabolism of other amino acids KEGG2, Neurodegenerative diseases KEGG2, Cofactors and vitamins Metabolism KEGG2, transport and catabolism KEGG2, endocrine system KEGG2, immune system diseases KEGG2, excretory system KEGG2, enzyme family KEGG2, membrane transport KEGG2, carbohydrate metabolism KEGG2, biosynthesis of other secondary metabolites KEGG2, terpenoids and polypeptides Metabolism of ketone compounds KEGG2, infectious diseases KEGG2, genetic information processing KEGG2, nervous system KEGG2, environmental adaptation KEGG2, nucleotide metabolism KEGG2, signaling molecules and interactions KEGG2, signal transduction KEGG2, transport and metabolism of inorganic ions KEGG3, Chromosome KEGG3, Cell cycle – Caulobacter KEGG3, Ribosome biogenesis KEGG3, DNA replication protein KEGG3, Transcription factor KEGG3, Glycine, serine and threonine metabolism KEGG3, Sulfur metabolism KEGG3, Other ion-coupled transporters KEGG3, Valine Biosynthesis of acid, leucine and isoleucine KEGG3, nitrogen metabolism KEGG3, biosynthesis of peptidoglycan KEGG3, homologous recombination KEGG3, peroxisome KEGG3, sulfur relay system KEGG3, peptidase KEGG3, protein Kinase KEGG3, mismatch repair KEGG3, xylene degradation KEGG3, ribosome KEGG3, RNA polymerase KEGG3, tryptophan metabolism KEGG3, histidine metabolism KEGG3, vitamin metabolism KEGG3, cell movement and secretion KEGG3, pyrimidine metabolism KEGG3, cytoskeleton Protein KEGG3, DNA replication KEGG3, amino sugar and nucleotide sugar metabolism KEGG3, folate biosynthesis KEGG3, carbon fixation in photosynthetic organisms KEGG3, phosphatidylinositol signaling system KEGG3, lysine degradation KEGG3, selenium compound metabolism KEGG3, Fructose and mannose metabolism KEGG3, inositol phosphate metabolism KEGG3, protein folding and related processing KEGG3, PPAR signaling pathway KEGG3, lipid metabolism KEGG3, valine, leucine and isoleucine degradation KEGG3, glyoxylic acid and Dicarboxylic acid metabolism KEGG3, arginine and proline metabolism KEGG3, limonene and pinene degradation KEGG3, D-alanine metabolism KEGG3, porphyrin and chlorophyll metabolism KEGG3, C5-branched dibasic acid metabolism KEGG3, molecular chaperones and folding catalyst KEGG3, fatty acid metabolism KEGG3, glutathione metabolism KEGG3, pentose phosphate pathway KEGG3, phosphotransferase system KEGG3, pantothenate and CoA biosynthesis KEGG3, proximal tubule bicarbonate recycling KEGG3, galactose metabolism KEGG3, Starch and sucrose metabolism KEGG3, primary immunodeficiency KEGG3, cysteine and methionine metabolism KEGG3, ubiquinone and other terpenoid quinone biosynthesis KEGG3, DNA repair and recombination proteins KEGG3, tyrosine metabolism KEGG3, benzene Alanine, tyrosine and tryptophan biosynthesis KEGG3, aminoacyl-tRNA biosynthesis KEGG3, alanine, aspartate and glutamate metabolism KEGG3, photosynthesis KEGG3, other transporters KEGG3, butyrate Metabolism KEGG3, bacterial secretion system KEGG3, glycerophospholipid metabolism KEGG3, oxidative phosphorylation KEGG3, type I diabetes KEGG3, glycolysis/gluconeogenesis KEGG3, photosynthesis protein KEGG3, transporter KEGG3, terpenoid skeleton biosynthesis KEGG3, unsaturated Fatty acid biosynthesis KEGG3, signal transduction mechanism KEGG3, ketone body synthesis and degradation KEGG3, nucleotide excision repair KEGG3, secretion system KEGG3, Alzheimer's disease KEGG3, zeatin biosynthesis KEGG3, type II diabetes KEGG3, D - Glutamine and D-glutamate metabolism KEGG3, Taurine and hypotaurine metabolism KEGG3, Glutamatergic synapse KEGG3, Plant-pathogen interaction KEGG3, Vitamin B6 metabolism KEGG3, Citric acid cycle KEGG3, B Benzene degradation KEGG3, base excision repair KEGG3, replication, recombination and repair protein KEGG3, eukaryotic ribosome biogenesis KEGG3, aminobenzoate degradation KEGG3, bacterial movement protein KEGG3, ansamycin biosynthesis KEGG3 , ion channel KEGG3, metabolism KEGG2, poorly characterized KEGG2, biosynthesis and biodegradation of secondary metabolites KEGG3, lipoic acid metabolism KEGG3, amino acid-related enzyme KEGG3, transcription protein KEGG3, ascorbate and alginate metabolism KEGG3, thiamine Vitamin metabolism KEGG3, unknown function KEGG3, glycosaminoglycan degradation KEGG3, other KEGG3, pentose and glucuronate interconversion KEGG3, biotin metabolism KEGG3, phenylalanine metabolism KEGG3, glycosphingolipid biosynthesis - ganglion series KEGG3, pore ion channel KEGG3, membrane and intracellular structural molecules KEGG3, purine metabolism KEGG3, via folate one-carbon pool KEGG3, phosphate and hypophosphite metabolism KEGG3, lysosome KEGG3, drug metabolism - other enzymes KEGG3, penicillins and cephalosporins Mycocin biosynthesis KEGG3, Huntington's disease KEGG3, niacinate and nicotinamide metabolism KEGG3, drug metabolism-cytochrome P450 KEGG3, lipopolysaccharide biosynthesis protein KEGG3, xenobiotic metabolism via cytochrome P450 KEGG3, tuberculosis KEGG3, poly Cyclaromatic hydrocarbons degrade KEGG3, or wherein the microbiome-related condition model includes a skin-related representation model generated based on the processed set of microbiome features, wherein the skin-related representation model is operable to determine a representation of a scalp-related condition for the user, and wherein the microbiome The set of group features includes features associated with at least one of the following: Actinobacteria (class), Lactobacillales (order), Actinobacteria (order), Firmicutes (phylum), Dermobacteriaceae (family ), Lactobacillus (family), Propionibacteriaceae (family), Corynebacteriaceae (family), Lactobacillus (genus), Corynebacterium (genus), Propionibacterium (genus), Dermatobacterium (genus) , Altococcus genus (genus), Corynebacterium freiburg (species), Altococcus crifolia KEGG3 (species), Corynebacterium species (species), Staphylococcus C9I2 species (species), Anaerobic cocci 8405254 species (species), Corynebacterium glucosinolyticum (species), Dermatobacterium hominis (species), Rhodobacteriaceae (family), Enterobacteriaceae (family), Staphylococcaceae (family), Enterobacteriaceae (order), Bacillus Bacilliformes (order), Bifidobacterium (genus), Staphylococcus (genus), Atropobacterium (genus), Megacoccus (genus), Corynebacterium mastitidis (species), Streptococcus BS35a species (species ), Fingoldella major (species), Staphylococcus aureus (species), Haemophilus influenzae (species), Corynebacterium NML 97-0186 species (species), oral taxon Streptococcus species G59 (species), Dorayella (genus), Roseberry species 11SE39 (species), Streptomyces dorylanin (species), Prevotellaceae (family), Veillonellaceae (family), Oscillator sp. Family (family), Gram-negative bacteria, Selenomonadales (order), Fingoldia (genus), Oscillator (genus), Enteromonas (genus), Frey Peptobacteria (genus), Prevotella (genus), Moyara (genus), Streptobacter glabrata (species), Collinsella aerogenes (species), Peptonephila 2002-2300004 species (species), Corynebacterium canis (species), Fingoldella sp. S9 AA1-5 (species), Prevotella buccal (species), Alisterella opacity (species), Moraxella sp. genus), Neisseria genus (genus), Neisseria mucus (species), Rikenmycetaceae (family), metabolism of cofactors and vitamins KEGG2, enzyme family KEGG2, lipid metabolism KEGG2, immune system diseases KEGG2, glycolysis Gluconeogenesis KEGG3, primary immunodeficiency KEGG3, pyruvate metabolism KEGG3, transport and catabolism KEGG2, neurodegenerative diseases KEGG2, endocrine system KEGG2, amino acid metabolism KEGG2, cellular processes and signaling KEGG2, signaling molecules and Interaction KEGG2, metabolism of other amino acids KEGG2, replication and repair KEGG2, transcription KEGG2, cell growth and death KEGG2, membrane transport KEGG2, biosynthesis of other secondary metabolites KEGG2, metabolism of terpenoids and polyketides KEGG2, inorganic Ion transport and metabolism KEGG3, vitamin metabolism KEGG3, biosynthesis of valine, leucine and isoleucine KEGG3, peroxisomes KEGG3, ribosome biogenesis KEGG3, selenium compound metabolism KEGG3, histidine metabolism KEGG3, chromosomal KEGG3, sulfur metabolism KEGG3, PPAR signaling pathway KEGG3, porphyrin and chlorophyll metabolism KEGG3, phosphatidylinositol signaling system KEGG3, inositol phosphate metabolism KEGG3, sulfur relay system KEGG3, glycine, serine and threonine Metabolism KEGG3, DNA replication protein KEGG3, pantothenate and CoA biosynthesis KEGG3, transcription factor KEGG3, protein folding and related processing KEGG3, type II diabetes KEGG3, protein kinase KEGG3, folate biosynthesis KEGG3, lysine degradation KEGG3, RNA polymerase KEGG3 , D-alanine metabolism KEGG3, carbon fixation in photosynthetic organisms KEGG3, nitrogen metabolism KEGG3, glycerophospholipid metabolism KEGG3, ansamycin biosynthesis KEGG3, valine, leucine and isoleucine degradation KEGG3 , Cytoskeletal protein KEGG3, peptidase KEGG3, fatty acid metabolism KEGG3, cell cycle - Caulobacter KEGG3, phosphotransferase system KEGG3, pyrimidine metabolism KEGG3, Alzheimer's disease KEGG3, butyrate metabolism KEGG3, tryptophan metabolism KEGG3 , signal transduction mechanism KEGG3, pentose phosphate pathway KEGG3, other ion-coupled transporters KEGG3, homologous recombination KEGG3, replication, recombination and repair protein KEGG3, xylene degradation KEGG3, mismatch repair KEGG3, glyoxylic acid and dicarboxylic acid Acid metabolism KEGG3, arginine and proline metabolism KEGG3, peptidoglycan biosynthesis KEGG3, molecular chaperone and folding catalyst KEGG3, type I diabetes KEGG3, DNA replication KEGG3, bacterial secretion system KEGG3, tyrosine metabolism KEGG3, Citric acid cycle KEGG3, amino sugar and nucleotide sugar metabolism KEGG3, ribosomal KEGG3, limonene and pinene degradation KEGG3, cell motility and secretion KEGG3, taurine and hypotaurine metabolism KEGG3, oxidative phosphorylation KEGG3, fructose and Mannose metabolism KEGG3, vitamin B6 metabolism KEGG3, ion channel KEGG3, ketone body synthesis and degradation KEGG3, other transport proteins KEGG3, galactose metabolism KEGG3, polycyclic aromatic hydrocarbon degradation KEGG3, transport protein KEGG3, DNA repair and recombination protein KEGG3, starch and sucrose metabolism KEGG3, alanine, aspartate and glutamate metabolism KEGG3, eukaryotic ribosome biogenesis KEGG3, secretion system KEGG3, unsaturated fatty acid biosynthesis KEGG3, cysteine and methionine Acid metabolism KEGG3, base excision repair KEGG3, aminobenzoate degradation KEGG3, photosynthesis KEGG3, photosynthesis protein KEGG3, pore ion channel KEGG3, lipid biosynthesis protein KEGG3, D-glutamine and D-glutamic acid Metabolize KEGG3. 6. A system for characterizing conditions related to microorganisms, the system comprising: Means for determining a microbial sequence data set for a user based on microbial nucleic acids from a sample associated with the user, the sample including a site diversity sample set corresponding to a plurality of collection sites, the collection site including the intestine , at least two of genitals, mouth, skin and nose, wherein said site diversity sample collection includes said sample from said user; and Based on the microbial sequence data set and the microbial-related condition model, the characterization of the microbial-related condition is determined for the user, and the microbial-related condition model is generated using a set of microbiome characterization modules and an application based on a set of analytical techniques to determine the microorganism-related condition. Parts of group feature collections, Wherein, the set of analysis techniques includes at least one of statistical testing, dimensionality reduction techniques and artificial intelligence methods, Wherein the microbiome characterization module set includes: a first microbiome characterization module operable to apply a first analytical technique of said set of analytical techniques, and a second microbiome characterization module operable to apply a second analytical technique of said set of analytical techniques, and wherein said characterization includes a set of site-wise disease susceptibility indicators based on said site diversity sample set and said microbial-related condition model, wherein each site-side set of site-side disease susceptibility indicators is Disease susceptibility indicators correspond to different collection sites of the plurality of collection sites and are associated with the microbial-related conditions. 7. The system of claim 6, wherein applying the set of analytical techniques to determine the set of microbiome characteristics includes: Determine an initial set of microbiome signatures based on the microbial sequence data set; and Using the first microbiome characterization module of the set of microbiome characterization modules, the dimensionality reduction technique is applied on the initial set of microbiome features to determine the set of microbiome features, wherein the dimensionality reduction technique Including at least one of missing value ratio, principal component analysis, probabilistic principal component analysis, matrix factorization technique, component mixture model and feature embedding technique. 8. The system of claim 7, wherein determining the initial set of microbiome characteristics includes applying the second microbiome characterization module of the set of microbiome characterization modules together with the microbial sequence data set. The statistical test is used to determine the initial set of microbiome characteristics, wherein the statistical test includes at least one of a t-test, a Kolmogorov-Smirnov test, and a regression model. 9. The system of claim 7, wherein applying the set of analytical techniques includes applying a machine learning method with the second microbiome characterization module of the set of microbiome characterization modules to determine the microbiome signature. A set of correlation scores, wherein the microbiome-related condition is generated based on the set of microbiome characteristics and the correlation score. 10. The system of claim 7, wherein determining the characterization includes based on the microbiome-related condition model, the sample from the user, and known correlations between the microbiome feature set and drug metabolism. , to determine drug metabolism signatures associated with the microbially relevant condition. 11. The system of claim 6, wherein determining a representation of the microbial-related condition for the user includes: Based on the set of site-wise disease susceptibility indicators, an overall disease susceptibility indicator is determined for the user, wherein the overall disease susceptibility indicator is associated with the microbial-related condition. 12. The system of claim 11, further comprising means for determining a microbial data set associated with the plurality of collection sites based on the site diversity sample set, wherein the overall disease susceptibility indicator is determined include: determining at least one of a covariance indicator and a correlation indicator based on the microbial data set, wherein at least one of the covariance indicator and the correlation indicator is associated with the plurality of collection sites; and The overall disease predisposition indicator is determined for the user based on the set of site-wise disease predisposition indicators and at least one of the covariance indicator and the correlation indicator. 13. The system of claim 6, wherein the microbial-related condition model is generated based on filtering of the microbial sequence data set by at least one of the following before applying the set of analytical techniques to determine the microbiome characteristics. : a) remove the first sample data corresponding to the first sample outlier of the sample set, wherein the first sample outlier is passed through at least one of principal component analysis, dimensionality reduction techniques and multivariate methods. b) remove the second sample data corresponding to the second sample outlier of the sample set, wherein the second sample outlier is determined based on the corresponding data quality of the microbiome feature set; and c ) removing a microbiome feature from the set of microbiome features based on a number of samples of the microbiome feature that do not satisfy a threshold sample number condition, wherein the number of samples corresponds to the number of samples associated with high quality data for the microbiome feature Number of samples. 14. The system of claim 6, wherein determining a representation of the microbial-associated condition for the user includes determining a photosensitivity-associated condition for the user based on a user microbiome feature set and the microbial-associated condition model. Skin-related representations, wherein the set of user microbiome features includes features associated with at least one of the following: Prevotella spp. (genus), Prevotella species WAL 2039G (species), Corynebacterium mastitidis ( species), Bacteroidetes (family), Blautia (genus), Bacteroides (genus), Desulfovibrio (genus), Clostridium (genus), Bacteroides vulgaris (species), Pula Faecalibacterium faecalis (species), Blautia faecalis (species), Mycobacterium putrificans (species), Bacteroidetes AR20 species (species), Bacteroides species AR29 (species), Bacteroides acidogenum (species), Dill Mycobacterium (genus), Schleckella (genus), Eggertia (genus), Adlerkreuzia (genus), Paraprevotella (genus), Mycobacterium Genus (genus), Haldemanella (genus), Eisenbergella (genus), Enterobacter (genus), Adlerkreuzia sp. (species), Succinic acid Coelacanthellae (species), Rosebaryllium glucovorans (species), Coelacanthobacter 377 species (species), Desulfovibrio pica (species), Eggertella HGA 1 species (species), Lactolipid Factor Long Lactobacillus species (species), Mycobacterium sp. HGB5 (species), Haldemanella filamentum (species), Collinsella enterica (species), Neisseria macaque (species), Clostridiaceae ( Family), Haemogeminis (species), Bacteroides fragilis (species), Enterobacteriaceae (family), Lachnospiraceae (family), Pasteurellaceae (family), Pasteurellales (order), Enterobacteriaceae (order), Sphingobacteriaceae (order), Haemophilus (genus), Leuconostoc (genus), Brevundumonas (genus), Prevotella oralis (species), Odorobacter (genus), Capnocytophaga (genus), Flavobacterium (genus), Pseudomonas brenneri (species), Cetiobacterium (species), Brevundumonas FXJ8.080 species (species), Ruminococcaceae (family), Vibrionaceae (family), Flavobacteriaceae (family), Fusobacteriaceae (family), Porphyromonadaceae (family), Brevibacteriaceae (family), Rhodobacterium Family (family), Intersporaceae (family), Bifidobacteriaceae (family), Sphingobacteriaceae (family), Caulobacteriaceae (family), Campylobacteriaceae (family), Bacteroidetes (class ), Fusobacteriaceae (class), Flavobacteriaceae (class), Bifidobacteriales (order), Neisseriales (order), Bacteroidetes (order), Rhodobacteriales (order), Flavobacteriales ( Order), Vibriales (order), Fusobacteriales (order), Caulobacteriales (order), Fusobacteria (phylum), Actinobacteria (genus), Campylobacter (genus), Fusarium chain Bacillus (genus), Brevibacterium (genus), Faecalibacterium (genus), Campylobacter (genus), Actinobacterium (genus), Porphyromonas (genus), Fusobacterium (genus) , Chryseobacterium (genus), Megacoccus (genus), Roseburia (genus), Neisseria (genus), Lactobacillus BL302 species (species), Bacteroides prebius (species), rod-shaped ulcers Bacillus (species), Campylobacter cambriciensis (species), Wechsler Blautia (species), Staphylococcus WB18-16 species (species), Oral taxon Streptococcus species G63 (species), V. Acidobacteria (species), Anaerobic cocci 9401487 species (species), Haemophilus parainfluenzae (species), Staphylococcus epidermidis (species), Campylobacter urealyticum (species), Janus species M3-5 (species), Prevotella timoniniensis (species), Peptonephila sp. NF00840 (species), Fingoldella sp. S8 F7 (species), Prevotella glycolytica (species), Porphyromonas cardinii (species), Fusobacterium periodontalum (species), infectious disease KEGG2, poorly characterized KEGG2, metabolic disease KEGG2, immune system disease KEGG2, cellular processes and signaling KEGG2, restriction endonuclease KEGG3, nucleotide excision repair KEGG3, or wherein determining the representation of the microbial-related condition for the user includes determining a skin-related representation of the dry skin-related condition for the user based on a user microbiome feature set and the microbial-related condition model, wherein the user microbiome The feature set includes features associated with at least one of the following: Corynebacteriaceae (family), Bacillus (class), Lactobacillales (order), Actinomycetes (order), Firmicutes (phylum), Corynebacterium (genus), Dermobacteriaceae (family), Lactobacilliaceae (family), Propionibacteriaceae (family), Actinobacteria (class), Dermobacterium (genus), Allisteria spp. (genus), Fecklemella spp. (genus), Lactobacillus (genus), Propionibacterium (genus), Corynebacterium ulcerans (species), Fecklemella hominis (species), Corynebacterium species (species) , Propionibacterium sp. MSP09A (species), Fecklamia sp. 1440-97 (species), Staphylococcus sp. C9I2 (species), Anaerobic cocci 9402080 species (species), Corynebacterium glucosinolyticus (species), Human Dermatobacteriaceae (species), Enterobacteriaceae (family), Pseudomonadaceae (family), Staphylococcus (family), Gammaproteobacteria (class), Bacillales (order), Enterobacteriaceae (order ), Bifidobacterium (genus), Pseudomonas (genus), Anaerobicococcus (genus), Kluyveria (genus), Atopia (genus), Staphylococcus (genus) ), Lactobacillus BL302 species (species), Corynebacterium mastitis (species), Bifidobacterium longum (species), Gemini anaerobic cocci species (species), Anaerobic cocci S9 PR-16 species (species), Timonin Prevotella (species), Kluyveromyces georgiana (species), Actinomyces (genus), Fingoldia (genus), Cronobacter (genus), Acinetobacter B22- 23 species (species), Anaerobic cocci Sarcina (species), Fingoldella S9 AA1-5 species (species), Staphylococcus aureus C-D-MA2 species (species), Peptonephila 7-2 species (species), Cronobacter sakazakii (species), Pasteurellaceae (family), class Acidobacteria (class), class Sphingobacteriaceae (class), order Sphingobacteriaceae (order), phylum Acidobacteria (phylum) , Porphyromonas (genus), Haemophilus (genus), Acinetobacter (genus), Anaerobic cocci 8405254 species (species), Sphingomonas (family), Sphingomonas order (order), Cookella (genus), Geminicoccus (genus), Veillonella CM60 species (species), Lactobacillus 7_1_47FAA species (species), Geminicoccus 933-88 species (species), Ca Porphyromonas formula (species), Haemophilus parainfluenzae (species), Bacteroides AR20 species (species), Bacteroides vulgaris (species), Bacteroides species D22 (species), Streptomyces multielongata (species), Parabacteroides faecalis (species), Bacteroidetes AR29 species (species), Doractella (genus), Collinsella (genus), Bacteroidetes (genus), Oscillatorspiraceae (family), Ruminococcaceae (family), Bacteroidetes (family), Verrucomicrobiaceae (family), Rhodobacteriaceae (family), Clostridiales (order), Bacteroidetes (order), Verrucomicrobiales (order ), Rhododendrones (order), Thermoanaerobes (order), Clostridiales (class), Bacteroidetes (class), Verrucomicrobia (class), Verrucomicrobia (phylum), Bacteroidetes Bacteria (phylum), Transcription KEGG2, Cellular processes and signaling KEGG2, Amino acid metabolism KEGG2, Cell growth and death KEGG2, Replication and repair KEGG2, Metabolism of other amino acids KEGG2, Neurodegenerative diseases KEGG2, Metabolism of cofactors and vitamins KEGG2 , transport and catabolism KEGG2, endocrine system KEGG2, immune system diseases KEGG2, excretory system KEGG2, enzyme family KEGG2, membrane transport KEGG2, carbohydrate metabolism KEGG2, biosynthesis of other secondary metabolites KEGG2, terpenoids and polyketides Metabolism KEGG2, infectious diseases KEGG2, genetic information processing KEGG2, nervous system KEGG2, environmental adaptation KEGG2, nucleotide metabolism KEGG2, signaling molecules and interactions KEGG2, signal transduction KEGG2, transport and metabolism of inorganic ions KEGG3, chromosome KEGG3 , cell cycle – Caulobacter KEGG3, ribosome biogenesis KEGG3, DNA replication protein KEGG3, transcription factor KEGG3, glycine, serine and threonine metabolism KEGG3, sulfur metabolism KEGG3, other ion-coupled transporters KEGG3, valine, Leucine and isoleucine biosynthesis KEGG3, nitrogen metabolism KEGG3, peptidoglycan biosynthesis KEGG3, homologous recombination KEGG3, peroxisome KEGG3, sulfur relay system KEGG3, peptidase KEGG3, protein kinase KEGG3 , mismatch repair KEGG3, xylene degradation KEGG3, ribosomal KEGG3, RNA polymerase KEGG3, tryptophan metabolism KEGG3, histidine metabolism KEGG3, vitamin metabolism KEGG3, cell movement and secretion KEGG3, pyrimidine metabolism KEGG3, cytoskeletal protein KEGG3 , DNA replication KEGG3, amino sugar and nucleotide sugar metabolism KEGG3, folate biosynthesis KEGG3, carbon fixation in photosynthetic organisms KEGG3, phosphatidylinositol signaling system KEGG3, lysine degradation KEGG3, selenium compound metabolism KEGG3, fructose and Mannose metabolism KEGG3, inositol phosphate metabolism KEGG3, protein folding and related processing KEGG3, PPAR signaling pathway KEGG3, lipid metabolism KEGG3, valine, leucine and isoleucine degradation KEGG3, glyoxylic acid and dicarboxylic acid Acid metabolism KEGG3, arginine and proline metabolism KEGG3, limonene and pinene degradation KEGG3, D-alanine metabolism KEGG3, porphyrin and chlorophyll metabolism KEGG3, C5-branched dibasic acid metabolism KEGG3, molecular chaperones and folding Catalyst KEGG3, fatty acid metabolism KEGG3, glutathione metabolism KEGG3, pentose phosphate pathway KEGG3, phosphotransferase system KEGG3, pantothenate and CoA biosynthesis KEGG3, proximal tubule bicarbonate recycling KEGG3, galactose metabolism KEGG3, starch and Sucrose metabolism KEGG3, primary immunodeficiency KEGG3, cysteine and methionine metabolism KEGG3, ubiquinone and other terpenoid quinone biosynthesis KEGG3, DNA repair and recombination protein KEGG3, tyrosine metabolism KEGG3, phenylalanine Acid, tyrosine and tryptophan biosynthesis KEGG3, aminoacyl-tRNA biosynthesis KEGG3, alanine, aspartate and glutamate metabolism KEGG3, photosynthesis KEGG3, other transporters KEGG3, butyrate ester metabolism KEGG3 , bacterial secretion system KEGG3, glycerophospholipid metabolism KEGG3, oxidative phosphorylation KEGG3, type I diabetes KEGG3, glycolysis/gluconeogenesis KEGG3, photosynthetic protein KEGG3, transporter KEGG3, terpenoid skeleton biosynthesis KEGG3, unsaturated fatty acids Biosynthesis KEGG3, signal transduction mechanism KEGG3, ketone body synthesis and degradation KEGG3, nucleotide excision repair KEGG3, secretion system KEGG3, Alzheimer's disease KEGG3, zeatin biosynthesis KEGG3, type II diabetes KEGG3, D-glutamine Aminamide and D-glutamate metabolism KEGG3, taurine and hypotaurine metabolism KEGG3, glutamatergic synapse KEGG3, plant-pathogen interaction KEGG3, vitamin B6 metabolism KEGG3, citric acid cycle KEGG3, ethylbenzene degradation KEGG3, base excision repair KEGG3, replication, recombination and repair protein KEGG3, eukaryotic ribosome biogenesis KEGG3, aminobenzoate degradation KEGG3, bacterial movement protein KEGG3, ansamycin biosynthesis KEGG3, ions Channel KEGG3, metabolism KEGG2, poorly characterized KEGG2, biosynthesis and biodegradation of secondary metabolites KEGG3, lipoic acid metabolism KEGG3, amino acid-related enzyme KEGG3, transcription protein KEGG3, ascorbate and alginate metabolism KEGG3, thiamine metabolism KEGG3, unknown function KEGG3, glycosaminoglycan degradation KEGG3, other KEGG3, pentose and glucuronate interconversion KEGG3, biotin metabolism KEGG3, phenylalanine metabolism KEGG3, glycosphingolipid biosynthesis-ganglion series KEGG3, Pore ion channel KEGG3, membrane and intracellular structural molecules KEGG3, purine metabolism KEGG3, folate one-carbon pool KEGG3, phosphate and hypophosphite metabolism KEGG3, lysosome KEGG3, drug metabolism-other enzymes KEGG3, penicillin and cephalosporin biology Synthesis KEGG3, Huntington's disease KEGG3, niacinate and nicotinamide metabolism KEGG3, drug metabolism-cytochrome P450 KEGG3, lipopolysaccharide biosynthesis protein KEGG3, xenobiotic metabolism via cytochrome P450 KEGG3, tuberculosis KEGG3, polycyclic aromatic hydrocarbon degradation KEGG3 ,or Determining the representation of the microbiome-related condition for the user includes determining a skin-related representation of the scalp-related condition for the user based on the user's microbiome feature set and the microbe-related condition model, wherein the user microbiome feature set Includes characteristics associated with at least one of the following: Actinomycetes (class), Lactobacillales (order), Actinomycetes (order), Firmicutes (phylum), Dermobacteriaceae (family), Lactobacilliaceae (family), Bacteriaceae (family), Propionibacteriaceae (family), Corynebacteriaceae (family), Lactobacillus (genus), Corynebacterium (genus), Propionibacterium (genus), Dermatobacterium (genus), another Corynebacterium sp. (genus), Corynebacterium freiburg (species), Altococcus krickellae KEGG3 (species), Corynebacterium sp. (species), Staphylococcus sp. C9I2 (species), Anaerobic cocci 8405254 species (species) , Corynebacterium glucosinolyticum (species), Dermatobacterium hominis (species), Rhodobacteriaceae (family), Enterobacteriaceae (family), Staphylococcus (family), Enterobacteriaceae (order), Bacillusles ( order), Bifidobacterium (genus), Staphylococcus (genus), Atropobacterium (genus), Megalococcus (genus), Corynebacterium mastitis (species), Streptococcus BS35a species (species), Fingoldia sp. (species), Staphylococcus aureus (species), Haemophilus influenzae (species), Corynebacterium NML 97-0186 sp. (species), Oral taxon Streptococcus sp. G59 (species), Doraxella sp. Genus (genus), Roseberry species 11SE39 (species), Streptomycin dolongata (species), Prevotellaceae (family), Veillonellaceae (family), Oscillatoriospiraceae (family) ), Gram-negative bacteria, order Selenomonadales (order), Fingoldia (genus), Oscillatorspira (genus), Enteromonas (genus), Freifeldt Bacteria (genus), Prevotella (genus), Moyara (genus), Streptobacter glabrata (species), Collinsella aerogenes (species), Peptonephila 2002-2300004 species (species) , Corynebacterium canis (species), Fingoldella sp. S9AA1-5 (species), Prevotella buccal (species), Alisterella opacity (species), Moraxella spp. (genus), Nei Serella (genus), Neisseria mucus (species), Rikenbacteriaceae (family), metabolism of cofactors and vitamins KEGG2, enzyme family KEGG2, lipid metabolism KEGG2, immune system diseases KEGG2, glycolysis/glycoisoglycolysis Biogenesis KEGG3, Primary immunodeficiency KEGG3, Pyruvate metabolism KEGG3, Transport and catabolism KEGG2, Neurodegenerative diseases KEGG2, Endocrine system KEGG2, Amino acid metabolism KEGG2, Cellular processes and signaling KEGG2, Signaling molecules and interactions KEGG2, Metabolism of other amino acids KEGG2, replication and repair KEGG2, transcription KEGG2, cell growth and death KEGG2, membrane transport KEGG2, biosynthesis of other secondary metabolites KEGG2, metabolism of terpenoids and polyketides KEGG2, transport of inorganic ions and Metabolism KEGG3, vitamin metabolism KEGG3, biosynthesis of valine, leucine and isoleucine KEGG3, peroxisomes KEGG3, ribosome biogenesis KEGG3, selenium compound metabolism KEGG3, histidine metabolism KEGG3, chromosome KEGG3 , sulfur metabolism KEGG3, PPAR signaling pathway KEGG3, porphyrin and chlorophyll metabolism KEGG3, phosphatidylinositol signaling system KEGG3, inositol phosphate metabolism KEGG3, sulfur relay system KEGG3, glycine, serine and threonine metabolism KEGG3, DNA Replication protein KEGG3, pantothenate and CoA biosynthesis KEGG3, transcription factor KEGG3, protein folding and related processing KEGG3, type II diabetes KEGG3, protein kinase KEGG3, folate biosynthesis KEGG3, lysine degradation KEGG3, RNA polymerase KEGG3, D-propanol Acid metabolism KEGG3, carbon fixation in photosynthetic organisms KEGG3, nitrogen metabolism KEGG3, glycerophospholipid metabolism KEGG3, ansamycin biosynthesis KEGG3, valine, leucine and isoleucine degradation KEGG3, cytoskeletal proteins KEGG3, peptidase KEGG3, fatty acid metabolism KEGG3, cell cycle - Caulobacter KEGG3, phosphotransferase system KEGG3, pyrimidine metabolism KEGG3, Alzheimer's disease KEGG3, butyrate metabolism KEGG3, tryptophan metabolism KEGG3, signal transduction Mechanism KEGG3, pentose phosphate pathway KEGG3, other ion-coupled transporters KEGG3, homologous recombination KEGG3, replication, recombination and repair protein KEGG3, xylene degradation KEGG3, mismatch repair KEGG3, glyoxylic acid and dicarboxylic acid metabolism KEGG3, Arginine and proline metabolism KEGG3, peptidoglycan biosynthesis KEGG3, molecular chaperone and folding catalyst KEGG3, type I diabetes KEGG3, DNA replication KEGG3, bacterial secretion system KEGG3, tyrosine metabolism KEGG3, citric acid cycle KEGG3 , amino sugar and nucleotide sugar metabolism KEGG3, ribosomal KEGG3, limonene and pinene degradation KEGG3, cell motility and secretion KEGG3, taurine and hypotaurine metabolism KEGG3, oxidative phosphorylation KEGG3, fructose and mannose metabolism KEGG3 , Vitamin B6 metabolism KEGG3, ion channel KEGG3, ketone body synthesis and degradation KEGG3, other transport proteins KEGG3, galactose metabolism KEGG3, polycyclic aromatic hydrocarbon degradation KEGG3, transport protein KEGG3, DNA repair and recombination protein KEGG3, starch and sucrose metabolism KEGG3 , Alanine, aspartate and glutamate metabolism KEGG3, eukaryotic ribosome biogenesis KEGG3, secretion system KEGG3, unsaturated fatty acid biosynthesis KEGG3, cysteine and methionine metabolism KEGG3, Base excision repair KEGG3, aminobenzoate degradation KEGG3, photosynthesis KEGG3, photosynthesis protein KEGG3, pore ion channel KEGG3, lipid biosynthesis protein KEGG3, D-glutamine and D-glutamate metabolism KEGG3. 15. The system of claim 6, wherein the microbiome-related condition includes a skin-related condition, wherein the system further comprises means for promoting probiotic therapy for the skin-related condition to the user based on the characterization , and wherein the probiotic therapy is associated with a microorganism associated with any of the following: Corynebacterium ulcerans, F. hominis, Corynebacterium sp., Propionibacterium sp. MSP09A, F. F. 1440 -97 species, Staphylococcus C9I2 species, Anaerobic cocci 9402080 species, Corynebacterium glucosinolyticum, Dermatobacterium hominis, Lactobacillus BL302 species, Corynebacterium mastitidis, Bifidobacterium longum, Gemini anaerobes, Anaerobic cocci S9 PR -16 species, Prevotella timoninensis, Kluyveromyces georgina, Acinetobacter WB22-23 species, Anaerobacter sarsinae, Fingoldella S9 AA1-5 species, Staphylococcus aureus C-D-MA2 species , 7-2 species of Peptonephila, Cronobacter sakazakii, 8405254 species of Anaerobic cocci, 60 species of Veillonella CM, 7_1_47 species of Lactobacillus FAA, 933-88 species of Geminicoccus, Porphyromonas cassinii, parainfluenza Haemophilus, Bacteroidetes AR20 species, Bacteroides vulgaris, Bacteroides species D22, Streptomycin dolongata, Parabacteroides faecalis, Bacteroides AR29 species, Prevotella species WAL 2039G, Faecalibacterium prauscii , Blautia faecalis , Mycobacterium putrefaciens , Bacteroides acidogenum , Adlerkreutzia succinic acidum , Koalabacterium succinic acidus , Rosebaryllis glucovorans , 377 species of Koalabacterium , P. Desulfovibrio, Eggertia species HGA1, Lactobacillus longum species, Mycobacterium species HGB5, Filamentous Haldemanella, Enterobacteriaceae Collinsella, Neisseria macaque, Geminicoccus sanguinis, Bacteroides fragilis, Prevotella oralis, Pseudomonas brenneri, Flavobacterium cetaceans, Brevundumonas species FXJ8.080, Bacteroides prebius, Campylobacter cambrianus, Wexloeb Lauterella, Staphylococcus WB18-16 species, Oral taxon Streptococcus species G63, Propionibacterium acnes, Anaerobic cocci 9401487 species, Staphylococcus epidermidis, Campylobacter urealyticum, Janus species M3-5, Peptonephila Bacteria DNF00840, Fingoldella species S8 F7, Prevotella glycopeptone, Fusobacterium periodontalum, Corynebacterium freiburgi, Alternococcus rickelae, Streptococcus BS35a species, Fingoldella macrophylla , Staphylococcus aureus, Haemophilus influenzae, Corynebacterium NML97-0186 species, Oral taxon Streptococcus species G59 species, Rosebaryllium species 11SE39 species, Streptobacter aureus, Collinsella aerogenes, Peptonephila species 2002-2300004 , Corynebacterium canis, Prevotella buccal, Alisterella opacity, and Neisseria mucus.